Ionospheric Threat Parameterization for Local AreaGlobal-Positioning-System-Based Aircraft Landing Systems

Seebany Datta-Barualowast

San Jose State University San Jose California 95192

Jiyun Leedagger

Korea Advanced Institute of Science and Technology Daejeon 305-701 Republic of Korea

and

Sam PullenDagger Ming Luosect Alexandru Enepara Di Qiupara Godwin Zhanglowastlowast and Per Engedaggerdagger

Stanford University Stanford California 94305

DOI 102514146719

Observations of extreme spatial rates of change of ionospheric electron content and the characterization strategy

for mitigation applied by the US local area augmentation system are shown During extreme ionospheric activity

the gradient suffered by a global navigation satellite system user a few kilometers away from a ground reference

station may reach as high as 425 mm of delay (at the GPS L1 frequency) per km of user separation The method of

data analysis that produced these results is described and a threat space that parameterizes these possible threats to

user integrity is defined Certain configurations of user reference station global navigation satellite system satellite

and ionospheric storm-enhanced densitymay inhibit detection of the anomalous ionosphere by the reference station

Nomenclature

B = satellite clock bias mb = receiver clock bias mD = ionospheric threat model maximum ionospheric delay mf1 = 157542 MHz GPS L1 frequencyf2 = 122760 MHz GPS L2 frequencyg = spatial rate of change of ionospheric delay also known

as slope or gradient mm=kmI = slant ionospheric delay along signal ray-path at a given

frequency (in this paper L1) mK = 403 m3 s2

N = integer ambiguity in number of cycles in carrier-phasemeasurement dimensionless

Ne = electron number density m3

r = true range between GPS satellite and receiver mrx = receiver locationsv = GPS satellite locationT = slant tropospheric error mt = timev = ground speed of ionospheric storm or wave front in the

ionospheric threat model m=sw = ionospheric threat model spatial width of linearly

varying region km

x = receiver position = receiver noise and multipath m = GPS carrier signal wavelength m = GPS pseudorange measurement mvig = nominal standard deviation of ionospheric error

broadcast to local area augmentation system usersmm=km

gd = GPS satellite group delay or interfrequency bias m = GPS carrier-phase measurement mi j = subscript GPS receiver indexk = superscript GPS satellite index

I Introduction

T HE US Federal Aviation Administration (FAA) is developingground-based augmentation of theGPS known as the local area

augmentation system (LAAS) to provide differential GPS (DGPS)corrections to single-frequency users within tens of kilometers of asingle airport [1] The key element of this system is the LAASgroundfacility (LGF) which is a DGPS reference station equippedwith fourGPS receivers (whose antennas are sited at known locations at theairport) and a vhf data broadcast link for sending corrections to usersA simple illustration of this configuration is shown in Fig 1 (thisfigure will be addressed again in Sec III to illustrate an ionosphericthreatuserLGFsatellite configuration) LAAS improves useraccuracy by eliminating errors common to the user and referencestations and also provides a guaranteed level of integrity

Integrity is an assurance provided to the user that bounds thedifference between the unknown true position and the LAAS-derivedposition estimate with an extremely high degree of confidenceAnomalies or system failures that would violate this bound must befollowed by a timely warning that either establishes a new bound orindicates that no guarantee can be made [2] These bounds arecomputed by users in the position domain based on informationbroadcast by the LGF and are known as the lateral and verticalprotection levels (VPL) Typically the closer a maneuver brings anaircraft to a runway (ie the ground) the more stringent the require-ments on integrity become In particular for aircraft approach andlanding procedures the vertical alert limits that the VPL must fallwithin are more stringent due to the presence of nearby obstacles

The current version of LAAS is designed to meet the demands inaccuracy availability and integrity needed for category 1 precisionapproaches To do this the system monitors many known error

Received 13 August 2009 revision received 29 April 2010 accepted forpublication 29 April 2010 Copyright copy 2010 by the American Institute ofAeronautics and Astronautics Inc All rights reserved Copies of this papermay be made for personal or internal use on condition that the copier pay the$1000 per-copy fee to the Copyright Clearance Center Inc 222 RosewoodDrive Danvers MA 01923 include the code 0021-866910 and $1000 incorrespondence with the CCC

lowastAssistant Professor Department of Aviation and Technology OneWashington Square SeebanyDatta-Baruasjsuedu

daggerAssistant Professor Department of Aerospace Engineering 335Gwahangno Yuseong-gu (Corresponding Author)

DaggerSenior Research Engineer Department of Aeronautics and AstronauticsDurand Building Room 250

sectResearch Engineer Department of Aeronautics andAstronautics DurandBuilding Room 250

paraResearch Assistant Department of Aeronautics andAstronautics DurandBuilding Room 250

lowastlowastResearch and Development Engineer Department of Aeronautics andAstronautics Durand Building Room 250

daggerdaggerProfessor Department of Aeronautics and Astronautics DurandBuilding Room 250 Member AIAA

JOURNAL OF AIRCRAFT

Vol 47 No 4 JulyndashAugust 2010

1141

sources including satellite ephemeris clock ionosphere andtroposphere and also estimates bounds on the uncertainty of theseerror sources

The GPS code and carrier-phase observables for the L1 signalfrequency (f1 157542 MHz) are [3]

1 rki bi Bk Tki Iki 11 rki bi Bk Tki Iki 1N1 1 (1)

The true range r between the ith receiver and kth satellite receiverclock bias b satellite clock bias B and tropospheric error T arenondispersive they do not vary with signal frequency The carrierphase contains an ambiguous integer number N of cycles ofwavelength of the carrier frequency f1 but has lower noise than thecode measurement (ie rsquo ) The ionospheric error I isfrequency-dependent and to first order is of equal magnitude butopposite sign on the carrier phase relative to the code phase

Of the various GPS error sources error due to ionospheric delay isone of the largest and most variable for single-frequency GPS usersThe ionosphere is a region of the upper atmosphere of Earth fromabout 50ndash2000 km in altitude that is ionized primarily by solarultraviolet radiation [4] Because of the free electrons and ions in thisregion electromagnetic signals (such as those broadcast by GPSsatellites) are refracted as they traverse the ionosphere The maineffect of this refraction is a delay in signal arrival timewith respect toan identical signal traveling through free space The cumulative delayis proportional to the total electron content (TEC) which is thedensity of electrons integrated along the signal path The resultingionospheric delay at L1 frequency I (in meters) can be expressed as

I K

f21

Zrx

sv

Ne dlK

f21TEC (2)

whereK 403 m3 s2 f1 is the carrier frequency of the signal (Hz)and the integral is of the electron density Ne (in electrons=m

3) overthe path length of the signal from satellite sv to receiver rx [5] Underordinary or quiet ionospheric conditions the ionosphere delays L1-frequency pseudorange measurements by one to several meters andadvances L1 carrier-phase measurements by an equal amount Iono-spheric activity varieswith location onEarth time of day season andphase within an 11-year cycle of solar activity

Under active ionospheric conditions such as local daytime duringthe peak of the solar cycle midlatitude ionospheric delays can reach30 m or more at zenith and 2ndash3 times that for a low-elevation ray-path which passes through more of the ionosphere Moreimportantly for LAAS the existence of large TEC values makes itpossible for the ionosphere to have low spatial correlation In such acase the ionospheric error suffered by the user may be very differentfrom the error suffered at the ground station

Figure 2 illustrates this potential magnitude and variability with amap of ionospheric delay over the eastern United States in the localafternoon during a magnetic storm at 2015 universal time (UT) on20 Nov 2003 Color contours correspond to the meters of rangingdelay error at the GPS L1 frequency from 0 (blue) to 20 m (red) The

processing used to generate such amap is detailed in [6] In short thismap is based on slant delays I measured from a network of GPSreceivers (see Sec IVonData) viewingmultiple satellites at differentelevations The point at which a line of sight (LOS) between areceiver and satellite reaches 350 km altitude is the ionosphericpierce point (IPP) which can be treated as an approximate location ofthe TEC producing that delay measurement Multiple measurementsare combined onto one plot via a geometric mapping function thatconverts elevation-dependent slant delays to equivalent vertical orzenith delays From Fig 2 it can be seen that equivalent zenithdelays can vary by tens of meters over a couple of degrees of latitudeand longitude during severe ionospheric disturbances

A number of regional and global models of the electron densityhave been developed within the scientific community In addition toentirely physics-based models [7ndash9] and empirical models (based onfitting parameters to historical data [10]) more recently data-assimilative techniques that update a model based on measurementsmade over time have advanced [11ndash13] However these do not yetcapture the structure and variability of the ionosphere during stormyperiods like the one shown in Fig 2 during which a storm-enhanceddensity (SED) with a poleward plume of ionization was observedover the conterminous United States (CONUS) Thus these modelscannot bound the impacts of ionospheric anomalies on theionospheric error I of an aviation userwith the high confidence levelsrequired for integrity

The LAAS LGF and aircraft separated by some distance on theorder of kilometers and viewing the same satellite each sufferspatially correlated ionospheric delays These simultaneous delayvalueswill differ slightly because theLOSof theLGFand the LOSofthe aircraft each pass through slightly different regions of theionosphere Theoretically in the limit as the LGF and airbornereceiver separation approaches zero this difference in ionosphericdelay vanishes

Early work in addressing the feasibility of GPS augmentationsystems in the presence of the ionosphere was done by Klobucharet al [14] The correlation of ionospheric delays was estimated to behigh enough that correction estimates could be made within a fewhundred kilometers of the userrsquos LOS [15] This principle allowsLAAS and the FAArsquos operational Wide Area Augmentation System(WAAS) to work in practice (note that WAAS unlike LAAS mustguarantee the integrity of ionospheric corrections over baselines ofhundreds of kilometers [16])

For LAAS Christie et al [17] worked to quantify the ionosphericdecorrelation rate for baselines of several kilometers This paperestimated based on data from several sites collected prior to 1999that the differential delay at L1 would be from a few mm to at most50 mm per kilometer of receiver separation Because the ionospheretypically varies smoothly for a user and LGF who are usuallyseparated by only a few kilometers the nominal ionospheric errorsigma broadcast to users (known as vig [18]) is only a few mm of

Iono speed v

70 ms

45 km LGF

GPS satellite

LGF IPP User IPP Iono Front

Fig 1 Illustration of ionosphere-induced error for a LAAS user

Fig 2 Map of equivalent vertical ionospheric delay over eastern

United States on 20 Nov 2003 at 2015 UT

1142 DATTA-BARUA ETAL

delay difference per km of user-LGF separation distance [19] TheLAAS integrity bound calculation includes this term and the needfor a model to quantify anomalous behavior that might escapedetection was not thought to be needed for baselines of less than50 km

However Datta-Barua et al [20] first investigated dual-frequencyGPS network data during an ionospheric storm in 2000 thatdemonstrated that the differential delay could possibly be hundredsof mm=km Luo et al [21] posited a model for the user-LGF-ionospheric threat scenario and demonstrated that differential delaysof hundreds of mm at L1 frequency per km could pose unacceptablyhazardous conditions to a LAAS user under certain geometries andrelative aircraft motions The observation and impact of such highspatial rates of change in delay over short baselines invalidated theprevious belief that the broadcast vig would bound anomalousionosphere Since boundingwas not possible an ionospheric spatial-gradient threat model for LAAS representing possible anomaliesthat could not be bounded by vig needed to be developed andmitigated to the degree that it threatened LAAS users Developingthis model took several years involved several researchers andrequired several iterations before culminating in the work presentedhere

In this paper we review the methodology of this data analysis andexplain the current ionospheric threat model for CONUS whichmust be mitigated by the LGF to meet the integrity requirements forCAT I precision approaches [2223] Sec II provides the motivationfor modeling anomalous ionospheric behavior namely the dis-covery that the differential error over baselines of a few kilometerscan be hundreds ofmm=km at L1 In Sec III we develop a model forthese spatio-temporal variations and define the fundamental param-eters that characterize this model Sec IV introduces the dual-frequency GPS data sets that are used in this work and describes theuses and relative strengths of each The algorithm for automatedanalysis and procedures for manual verification are investigated inSec V which is the most time-intensive part of our study Thefinalized threat space of this model incorporating the extremeparameter values are summarized in Fig 13 of Sec VI Sec VIIcontains our concluding remarks

II Severe Ionospheric GradientsDiscovered in CONUS

The discovery of spatial rates of change shown in this section thatare more than 2 orders of magnitude higher than nominal

decorrelation rates motivates the need for an ionospheric threatmodel because such gradients could never be bounded by a practicalbroadcast sigma value Measurements of ionospheric delay or TECcan be made with dual-frequency ranging receivers which takeadvantage of the dispersive nature of the ionosphere as given byEq (2) For dual-frequency GPS receivers the observables at the L1frequency are given by Eq (1) and the observables at the secondfrequency (in this paper f2 122760 MHz) are

2 rki bi Bk Tki Iki IFBi kgd 22 rki bi Bk Tki Iki IFBi kgd 2N2 2

f21

f22

(3)

The range r clock biases b and B and tropospheric error T areidentical to those in Eq (1) Similar noise terms and integerambiguitiesN exist at the L2 frequency The ionospheric delay at theL2 frequency is proportional to the delay I at the L1 frequency by thesquared frequency ratio Additionally receiver hardware inter-frequency biases (IFB) and satellitegd affect the estimate of delay

The slant ionospheric delay at L1 can be computed from the GPScode and carrier observables at L1 and L2 frequencies given inEqs (1) and (3) in three ways

I 2 1 1

I 1 2 1

ICMC 1 1

2(4)

The code estimate I is noisy because but has no integerambiguity The carrier estimate I is low-noise but contains integerambiguities for both L1 and L2 The single-frequency carrier minuscode (CMC) or code-carrier divergence estimate ICMC is robust tothe fragility of L2 codeless and semicodeless tracking loops butcontains both the L1 integer ambiguity and noise In the dual-frequency estimates I and I the receiver and satellite hardwarebiases (IFB and gd) must be removed

For this initial study high-quality ionospheric data for theCONUSregion from the FAA WAAS network were used WAAS had 25ground stations over the United States during the period underinvestigation Each ground station has three L1ndashL2 dual-frequencyreceivers allowing for a directmeasurement of the ionospheric delayThe raw receiver carrier-phase data is leveled to the code-phase datain postprocessing to remove integer ambiguitiesN1 andN2 Satellitebias gd and receiver interfrequency bias IFB must be estimated and

Fig 3 Map of CORS station (dots) and WAAS reference station (diamonds) locations

DATTA-BARUA ETAL 1143

removed separately [24] WAAS receiver redundancy allows forvoting to remove possible artifacts due to problems on individualreceivers These data are reliable but geographically sparse relative tothe LAAS service area as the closest WAAS reference stations are255 km apart These locations are denoted with diamonds in Fig 3

To estimate the spatial rate of change possible in a LAASconfiguration the difference in delay along two lines of sight (LOSs)with a distance between them that is characteristic of LAAS must bemeasured One technique that is especially useful for analyzingdifferential delayswhen receivers are sparsely distributed is the time-stepmethod in which the IPP traversal distance over time substitutesfor the LAAS ground stationaircraft separation [19]

WAAS data was used to identify times and regions exhibiting highrates of change as computed by the time-step method during theionospheric storm of 6 April 2000 [20] During this storm a largeSED passed over CONUS and its duskward edge caused highdifferential delays Figure 4 shows the slant ionospheric delay inmeters at each of the threeWAAS receivers at ZDCas they track SVN40 The horizontal axis below each subplot marks the elapsed time inseconds For each receiver I (red) I (blue) and ICMC (green) areplotted Both I and ICMC are recentered to the time-averaged codemeasurement of the delay I to account for the ambiguous integernumber of cycles

Over a time interval of 110 s demarcated with vertical lines onFig 4 the receiver tracked through a drop in the slant ionosphericdelay of about 8 m There were no cycle slips (discontinuities in themeasured ionospheric delay due to temporary receiver trackingproblems) for any of the receivers during the time interval inquestion The identical time evolution shown by all three receivers onall three forms of estimated delay indicates that the observedanomaly was not due to receiver bias

A similar event for an independent LOS an hour and a half earlieron the same day further confirms the ZDC-SVN 40 anomaly Thereceiver measurements of the ionosphere between theWAAS stationZBWand SVN 24 show that while station ZBW was tracking SVN24 during a span of 150 s the slant ionospheric delay on this LOSdropped by about 10 m Given SVN 24rsquos position in the sky duringthis time interval the IPP of the ray-path traveled about 15 km Thefact that these observations represent two independent LOSsindicates that neither biases at an individual receiver or satellite norcycle slips contributed to the high apparent decorrelation Thereforethe anomalous events that affected LOS ZDC-SVN 40 and LOSZBW-SVN 24 were both records of actual ionospheric events

However the gradients they imply (8m at L1 over 7 km separationand 10 m over 15 km) may be artificially inflated for the followingreason The anomaly at station ZBWas it tracked SVN 24 precededthe anomaly along LOS ZDC-SVN 40 by 15 h Ionospheric TECmaps for this storm period shown in the Appendix to [25] roughlyindicate that the storm front whose boundary runs roughly east-westrecedes southward for the duration Having first been traversed by anIPP from station ZBW it progresses south to be traversed by an IPPfrom ZDC 15 h later Based on this observation to first order its

velocity vfront 110 m=s southward The IPP associated with ZDC-SVN 40 was moving primarily northward with ground speedvIPP 7 km=110 s 63 m=s respectively The velocity of thestorm front is nonnegligible compared with the IPP velocity Sincethe velocities were directed roughly opposite to each other theadditive effect of the relative velocities would make the spatialgradient in the ionosphere appear to be a factor of two ormoreworsethan it actually was Instead of a 7 kmwidth thewidthw of the stormfront was more likely

w vfront vIPP t 110 63 110 19 030 m (5)

This means that the purely spatial gradient of the ionosphere wasmore likely 8 m of slant delay difference over a 19 km range w asshown in Eq (5) giving a 420 mm=km spatial rate of change of slantdelay

From this event we learned about the potential existence of spatialrates of change in ionospheric delay with magnitudes that could notbe bounded by the LAAS nominal broadcast correction We alsoobserved that estimating the spatial rate of change is complicated bythe mixing of spatial and temporal variations in the ionospherethrough SED motion as well as the motion of LOSs relative to theionospheric structure

III Ionospheric Threat Model Parameterization

The discovery of high spatial gradients at the edges of ionosphericSEDs combined with relative motions of the ionosphere and LOSswas used to develop an ionospheric threat model for identifyingpossible threats to LAAS users In this section we define a model foran ionospheric wave front and associated model parameters

Based on the findings in Sec II the ionospheric threat is modeledas a spatially linear semi-infinite wave front with constantpropagation speed Figure 5 illustrates this model The model isparameterized by the slope g of the ramp and its widthw the productof which yields a maximum ionospheric delay D and the groundspeed v of wave front The observations in Fig 4 of a constant highdelay a period of decline at a fairly constant rate and a period ofconstant low delay supports the piecewise linear model A higherorder model may fit the actual data better However simplicity in themathematical form of an ionospheric threat model enables easierassessment of impact on users through LAAS simulation tools andidentification of mitigation strategies

Themaximum ionospheric delay difference is upper-bounded by amaximum value which was set based on observations Slope andwidth values whose product creates a maximum delay differenceabove the bound on D are not part of the threat model and arediscarded This limit is conservative because it allows for possibleionospheric configurations that may be physically impossible but itkeeps the simulation search space tractable In any case as Luo et al[26] showed the extreme regions of the search space will be easilydetected by the LGF within the time to alarm when the ionosphericfront is verywide and thus sweeps across thefield of viewof the LGFFortunately the impact of ionospheric wave fronts on LAAS is notvery sensitive to width thus the choice was made to include a rangeof possible frontwidths from25 to 200 km in the threatmodel Largerwidths would not produce gradients sufficiently sharp to bethreatening and smaller widths would not cause much difference inLAAS impact simulation results [27] Thus data analysis proceduresare devoted to estimating the slopeg and speed v of ionosphericwavefront (Sec V)

Fig 4 Slant ionospheric delay in m between WAAS receivers at

Washington DC and GPS satellite 40 on 6 April 2000

Max Iono delay

D = w g

Front Speed (v )

Nominal IonoWidth (w)

Iono Front

Slope (g)

Fig 5 Ionospheric wave-front model

1144 DATTA-BARUA ETAL

As noted above the gradient is assumed to represent a linearchange in slant ionospheric delay between maximum and minimumdelays As a result the bounding slant gradients are also (at leastpotentially) functions of satellite elevation

To recap this threat model assumes severe ionospheric spatialgradients take the form of straight horizontally semi-infinite wavefronts of linearly varying TEC that move at constant speed anddirectionwith respect to the ground In addition the slope of the rampitself is not time-varying These are simplifications of reality but arereasonable for the duration of an aircraft approach These assump-tions are adequate because deviations from them do not materiallychange the final result except if most-evil variations (compoundingthe worst-case upon the worst-case) are allowed

The ionospheric threat model does not include other parametersthat are necessary for simulating and assessing impact on LAAS-equipped aircraft such as the orientation of the ionospheric wavefront with respect to the aircraft and runway the horizontal approachspeed of the aircraft the approach direction of the ionospheric frontthe initial position of the ionospheric front with respect to the aircraftat the start of the simulation and the velocities of the IPPs which aredriven by satellite and receiver motion Figure 1 illustrates anexample of one possible configuration of the ionospheric threatmodeluserrunwaysatellite scenario

IV Data

The remaining sections of this paper are concerned withidentifying theworst observed values of the ionospheric threatmodelparameters of slope and speed at different ranges of elevation anglesExisting ionospheric models do not capture anomalous behaviorsuch as that observed on 6 April 2000 so our threat-space devel-opment for the ionosphere is data-driven Precise estimates ofionospheric delays can be obtained using dual-frequency GPSmeasurements from networks of stations and sophisticated post-processing algorithms

The US National Geodetic Survey makes dual-frequency GPSdata publicly available in RINEX format from a network ofcontinuously operating reference stations (CORS) [28] CORS hashundreds of stations in CONUS (sites as of 29 Oct 2003 are shownin Fig 3) and permits ionospheric measurement comparisons acrossbaselines of tens of kilometers Before analysis geographicalclusters of the highest-density receiver regions in the United Statesare targeted to provide the best possible spatial resolution Eightclusters overCONUSof around 21 square degrees of average area aredefined and boxed in Fig 3 Each of these clusters contains 10ndash30receivers with baseline separations of 15ndash200 km Most of the dataanalysis is performed for stations inside these clusters In some casesadditional stations adjacent to these clusters are also used to bettercapture ionospheric wave-front behavior that extended beyondcluster boundaries

The dates fromwhich data were collected and analyzed are shownin Table 1 along with the publicly available indices of globalgeomagnetic activity planetary K (Kp) and disturbance storm time(Dst) and geomagnetic storm class (G-class) and WAAS coverageParticular geographic regions of focus if applicable are shown

The values of Kp Dst G-class and WAAS coverage are used tohelp target days onwhich anomalous behavior in CONUSwas likelyto have occurred Because the ionosphere is coupled to themagnetosphere which is in turn driven by the solar wind measuresof geomagnetic activity provide information about the ionosphere byproxy The Dst index measures activity in the worldwide geomag-netic field [2930] with negative deviations indicating ionosphericcurrents and activity Kp is a three-hour composite index based ongeomagnetic activity measured at several midlatitude stationsprimarily located in the northern hemisphere [3132] Kp ranges inthirds of an index unit from 0 (no activity) to 9 (extreme activity) TheSpace Weather Prediction Center of the National Oceanic andAtmospheric Administration has developed a geomagnetic stormscale and associated descriptors for use by both the technicalcommunity and the general public [33] The scale varies from G1throughG5 and the storm classes associated with each level in orderof increasing intensity are minor moderate strong severe andextreme

The percentage of WAAS coverage indicates the degree to whichthese storms introduced nonlinear spatial variations over CONUS(viewed over separations of hundreds of km between WAASreference stations) limiting WAAS availability for precision ap-proach While the other measures shown in Table 1 are globalWAAS coverage is a daily summary value that indicates how muchimpact each storm had in the American sector where the CORSstations are sited For the dates occurring before WAAS initialoperating capability (IOC) official coverage percentages do not exist

The data collected from the CORS network have been assimilatedand had biases removed by the use of the global ionosphericmapping(GIM) software atNASArsquos Jet PropulsionLaboratory (JPL)With theadditional use of the publicly available International GNSS Servicestation data worldwide GIM estimates and removes satellite andreceiver interfrequency biases to provide high-precision ionosphericmeasurements The dual-frequency ionospheric observables IDF arecomputed by leveling the phase estimates I using the codeestimates I The processing with a leveling function f is describedin detail in [34] The resulting data are provided at 30 s intervals

IDF fI I (6)

CORS ionospheric estimates are processed in a similar manner tothose ofWAAS (see Sec II) but the lack ofmultiple receivers at eachsite means that anomalies due to receiver glitches cannot be votedout In addition the quality of CORS receiver equipage and siting ismuch more variable than that of WAAS reference stations thustypical CORS receiver measurement errors are several times higherthan that of WAAS receivers The screening algorithms used at JPLare based on an error detection threshold that was set to allow moredata points and minimize the risk of discarding true ionosphericevents

V Data Analysis Procedure

The objective of this section is to produce the LAAS threat spaceon parameters g and v using the data from closely-spaced CORSstations described above The procedure for processing CORS

Table 1 Ionospheric storm dates and conditions investigated

Day (UT mmddyy) Kp Dst Geomagneticstorm class

WAAS coverage Focus region

040600 83 287 Severe None (pre-IOC) NE corridor040700 87 288 Extreme None (pre-IOC) NE corridor071500 90 289 Extreme None (pre-IOC) mdashmdash

071600 77 301 Strong None (pre-IOC) mdashmdash

090702 73 163 Strong None (pre-IOC) mdashmdash

102903 90 345 Extreme 0 mdashmdash

103003 90 401 Extreme 0 TXndashOKndashLAndashAR103103 83 320 Severe 0 FLndashGA112003 87 472 Extreme 0 OHndashMI071704 60 80 Moderate 688 TXndashOKndashLAndashAR

DATTA-BARUA ETAL 1145

receiver data identifying anomalously high spatial rates of changevalidating anomalies that are due to ionospheric events andestimating parameters in terms of the threatmodel of Sec III is shownin Fig 6 The starting point is raw L1 and L2 measurements fromselected clusters of CORS stations An automated algorithm flowingdown the left-hand side of the figure involves using the JPL-processed CORS-based estimates of ionospheric delay to computeapparent spatial gradients and front speeds and screen them forobvious receiver or data-processing errors The remaining points arecompared manually (right-hand side of Fig 6) to estimates derivedfrom raw CORS data The following sections trace through themethodology outlined in the flowchart in Fig 6

A Ionospheric Spatial-Gradient Estimation

1 Station Pair Method

Our approach involves considering selected pairs of CORSstations in the JPL-processed database as though they represent anLGF-user receiver pair (with a static user position) This techniquecalled the station pair method is illustrated in Fig 7 For each epocht the delays at each of two stations i and j viewing the same satellite kare differenced The slope of ionospheric delayrI between all pairsof receivers looking at each satellite is estimated by dividing thedifferential slant delay by the baseline distance between the tworeceivers

rIt jIki t Ikj tjkxi xjk

(7)

The station pair method unlike the time-step method (Sec II)does not artificially inflate ionospheric gradients due to relativemotions of IPPs and the ionosphere [19]

The dual-frequency data in each CORS cluster are separated intofive elevation bins 0ndash12 12ndash20 20ndash30 30ndash45 and 45ndash90 deg Foreach LOS elevation range receivers in each cluster are grouped intopairs and IDF is used for I in Eq (7) to give a dual-frequencyestimate of the gradient rIDF

2 Automated Screening

AfterrIDF is computed an automated screening process (bottom-left corner of Fig 6) isolates the most credible events above a chosenthreshold (100 mm=km) and eliminates those gradients that areapparently due to nonionospheric causes Cases for which tworeceivers are effectively collocated (less than 100m apart) or the slantgradient rIDF does not vary in time are attributed to an interreceiverbias and eliminated Cases for which ionospheric delay measure-ments from one receiver do not vary in time are attributed to a faultyreceiver and eliminated

Variations greater than 15 mm=s ionospheric delay rate of changeare flagged for possible ionospheric activity The 15 mm=s thresholdis set to be an order of magnitude lower than the largest time rate ofchange observed for the 29 Oct 2003 storm day [35] in order toallow significant rapid changes in the delay value to pass screening aspossible ionospheric events The advantage of this threshold is thatreal ionospheric events are less likely to be eliminated as falsenegatives The tradeoff is that false alarms occur when the datacontain cycle slips causing the delay value to jump suddenlybetween two successive measurement points These cases have to beeliminated manually from the screening results

3 Manual Validation

The plausible ionospheric events that survive the computer-automated screening process are analyzed manually For each ofthese events visual analysis determineswhether themagnitude of thegradients together with the length of time that these gradientsmanifest themselves look reasonable or aremore likely to be artifactsof faulty receiver tracking

The first step in verifying an event is by comparison againstsimultaneous observations of slant delay from other nearby receiversin the same cluster This can be thought of as cluster-wide voting thatoperates in a similar manner to the voting among colocated receiversat each WAAS reference station If other nearby CORS receiversexhibit similar ionospheric delay patterns to the pair that shows themost-extreme spatial decorrelation the event is very probably realand is not limited to a single receiver fault On the other hand if theobservations were influenced by receiver artifacts in the data it isunlikely that amajority of the receivers in the cluster contain the sameor similar artifacts

To support this comparison plots are constructed that extendacross the entire period of time for which data was extracted so thatthe receiver behavior can be analyzed both in the presence ofdisturbed ionosphere and also during calm ionospheric conditions oneither side of the disturbance While the electron content within theionosphere is expected to vary wildly during the actual storm delaymeasurements from the different receivers within a CORS cluster areexpected to converge to similar values at times of nominalionospheric activity Quiet periods of ionospheric behavior help toestablish a baseline for the affected receiver cluster and give anindication of any possible interreceiver biases

The next step in validating extreme gradients (top-right corner ofFig 6) is to estimate approximate wave-front parameters from rawL1 code-minus-carrier measurements ICMC and compare them to theobservations from the JPL-processed dual-frequency data IDF TheL1-only estimates of these slopesrICMC given by using ICMC for theslant delay I in Eq (7) provide a picture of gradients that is notsubject to fragile L2 tracking loops The L1-only estimates are notexact however because the integer ambiguity N1 has not beeneliminated

Two methods are used to remove the integer ambiguity for high-elevation and low-elevation LOSs respectively For high elevationthe postprocessed L1L2 estimate of absolute differential sloperIDFat a timewell before or after thewave front passes is used to initializethe single-frequency ionospheric gradient estimates rICMC A pointin time is picked at which the measurements rICMC are aligned torIDF by subtracting a constant bias from all the measurement of oneof the receivers equalizing the value that they measure at the choseninstant Since the differential delay between a pair of receivers isobserved this is equivalent to choosing a reference zero-slope point

L1 code and carrier

L1 code and carrier L2 carrier

ldquoRawrdquo IGSCORS Data

JPL Ionosphere ldquoTruthrdquo Processing

Ionosphere Delay Estimates

Find Maximum Apparent Gradients

(Station Pair Method)

Screening Process (Automated)

Database of Extreme Gradients Erroneous Receiver

Steps and L1L2 Biases Removed Investigate

Remaining Points

Remove ldquoQuestionablerdquoObservations

Estimate Gradients from L1 code-minus-carrier

Output Database of Maximum Gradients

Compare L1L2 and L1 CMC Gradient Obs

ldquoValidatedrdquoMax Gradient

Database

Fig 6 Ionospheric anomaly data analysis procedure

Fig 7 Station pair method Not to scale

1146 DATTA-BARUA ETAL

in rICMC at a particular instant in time and then expressing all otherslope measurements relative to it Thus L1 CMC ionosphericgradient estimates form lower bounds on the true gradient but theseestimates are accurate enough that they can be used to validate theL1L2 observations

For low-elevation satellites the track may not be long enough tosample through quiet ionosphere In this case the single-frequencygradient estimate is leveled to the mean of the JPL-processed dual-frequency estimate rIDF as shown in Eq (8)

r ~ICMCt rICMCt rICMC rIDF (8)

Anomalous ionospheric events not eliminated by this final manual

comparison to single-frequency estimates r ~ICMC of the spatialvariation are deemed to be validated measurements rIDFValidated andare used to generate bounds on the threat model parameter g

gmaxtrIDFValidated (9)

B Ionospheric Front Speed Estimation

Observations of severe gradients that are validated as described inSec VA are studied inmore detail to characterize them in the contextof the ionospheric threatmodel geometry The automated code used athree-station-based trigonometric algorithm to estimate the speedand direction of wave-front propagation with respect to the ground(see [22] for details) The travel time of the frontwhich swept a pair ofstations is measured to compute the speed and the third station isused to observe the front direction This algorithm proved to be verysensitive to measurement errors and limitations of the threat model(in particular the assumption that the wave front is a straight line)when extrapolated to cover three (instead of two) stations

To overcome the weakness of the automated ionospheric frontspeed computation method a manual two-station-based method isused that takes wave-front orientation (estimated manually based onmany stations) as an input so that only speed needs to be solved forFirst we form groups that typically include four to eight nearbyCORS receiverswhich show similar ionospheric delay patterns to thesatellite-receiver pair from which anomalous gradients wereobserved and verified By observing the time history of ionosphericslant delays from the stations observing the same satellite the peakdelay times tpeaki at which the slant delay of the station i reached thelocal maximum value are determined Because ionospheric delays ofthese nearby stations exhibit similar patterns the order in which thestations were affected by the ionospheric front can be deduced fromtpeaki This order and the locations of the affected stations make itpossible to approximately determine the forward propagationdirection of the front Since it was assumed that the wave front is astraight line and moves with constant speed which is not true inreality the sequence and tpeaki must be carefully adjusted manuallyAlso the slant delays sometimes appear to have more than one localmaximum and this ambiguity makes room for tpeaki to be fine-tunedconsidering the underlying assumptions

Next we select a pair of stations i j and determine the traveltime tij given by Eq (10)

tijinej tpeaki tpeakj i j 1 2 N (10)

The distance between the two stations projected onto the lineperpendicular to the wave frontdn is used to compute the speed ofthe wave front vn as shown in Eq (11)

vn dn

tijinej(11)

Note that this speed estimate contains the velocity componentresulting from the movement of the satellite in addition to that fromactual wave-front motion Thus the (notional) IPP velocity iscomputed and removed so that the resulting wave-front velocityestimates are referenced to a fixed point on the ground (such as anLGF site) IPP velocity and direction are computed from the CORS

data which give the notional IPP locations for each satellite at 30 sintervals The purpose of using IPP velocity even though the IPPconcept is not otherwise used in defining this threat model is tocapture in an average sense the speed of the ray-path motion throughthe ionosphere We repeat this computation for all combinations ofstations and obtain a range of front-speed estimates that are normal tothe ionospheric front Finally we take themean value of the resultingspeed estimates which is the manually estimated and validatedwave-front velocity with respect to the ground

VI Results CONUS Ionospheric AnomalyThreat Model

In this section we analyze two case studies following the processdescribed in Sec V These two cases are of particular interest becausethey give the maximum dual-frequency verified slopes we observedat high elevation (Sec VIA) and at low elevation (Sec VIB)respectively Both cases were observed from the OhioMichigancluster shown in the inset of Fig 3 and show very weak elevationdependence We then summarize the resulting threat model forCONUS

A Case Study 1 High Elevation

Figure 8 shows the slant ionospheric delay from this event asobserved by seven CORS reference stations in northern Ohio andsouthern Michigan as they tracked GPS SVN 38 Note the rapidgrowth in delay associatedwith the passage of the leading edge of theplume of enhanced delay shown in Fig 2 in just under an hourfollowed by an interval of erratic variation in ionospheric delaywithin the plumewhile the overall delay remains high followed by asudden steep drop-off corresponding to the duskward edge of theplume The JPL-processed dual-frequency delays (Fig 9a) andelevation angles (Fig 9c) over time are shown for ZOB1 (red) andGARF (green) which are separated by only 512 km The dual-frequency estimate of the sloperIDF between these stations is shownin Fig 9b

Data outages on both ZOB1rsquos and GARFrsquos dual-frequencymeasurements are visible in Fig 9a The drop from 30 to 18 m on theGARF (green) curve at 2055UTand on ZOB1 (red) at 2100 UT callsinto question the reliability of the dual-frequency estimate of theslope For this reason we validate the presence of an actual iono-spheric spatial anomaly within this data by comparing rIDF (blue)with the single-frequency estimate of the slope rICMC (red) inFig 10 The data outages present from 2055 to 2100 UT in rIDF donot exist in the L1-only measurements confirming that the data gapsare limited to L2measurements The general trend over time betweenthe two slope measurements agrees including the time during whichL2 data outages occur Based on the similarity of this event toobservations (Fig 8) by other receivers in the vicinity we can

Fig 8 Dual-frequency estimates of slant ionospheric delay from JPL-

processed CORS data vs UT hour for select stations in the Ohio

Michigan cluster tracking SVN 38 at high elevation

DATTA-BARUA ETAL 1147

confirm amaximum slope observed due to this event of 413 mm=kmat 2100 UT

B Case Study 2 Low Elevation

We also investigate data to show the largest-magnitude ion-ospheric gradients that we have validated at low elevation (less than15 deg) at 2130 UT Figure 11 is a plot of the dual-frequencymeasurements of slant ionospheric delay over time at these stationswhen observing SVN 26 to the northwest at about 12 deg elevation(not shown) The two stations FREO and WOOS (see inset of Fig 3for location) begin at 15 m and rise to over 45 m of slant delay as the

LOSs from these stations to SVN 26 pass through the filament ofenhanced ionospheric delay

We analyze the WOOSndashGARF pair of stations to estimate theionospheric spatial gradient between them The dual-frequency-based slope rIDF is plotted in blue on Fig 12 The slope rises from0 mm=km to nearly 400 mm=km as the delay for WOOS rises to45 m during passage through the anomalous region while GARFholds steady at about 15ndash18m There is a dual-frequency data gap forGARF at 2054 UT that contributes to the data gap inrIDF howeverthe outage is not simultaneous with the highest slope observedbetween this station pair

The dual-frequency estimate is compared with the single-frequency estimate of the ionospheric spatial gradientrICMC (greenon Fig 12) Continuous single-frequencymeasurements exist exceptat one epoch at 2052 UT at which time the L1 measurements had avisible cycle slip that was manually removed Based on theagreement between the two measurements we conclude that thehighest slope between the stationsWOOS and GARF occurs at 2120UT and is about 360 mm=km

C LAAS Ionospheric Threat Space

Using the method described in Sec V and illustrated in thesubsections above numerous other instances of high differentialdelay between receivers closer than 100 km were investigated frommultiple storm days in CONUS listed in Table 1 This was amultistage multiperson process spanning data from storms from

20 202 204 206 208 21 212 214 216 218 220

10

20

30

40Dual-frequencyZOB1 GARF SV38

Sla

nt

del

ay (

m)

ZOB1

GARF

20 202 204 206 208 21 212 214 216 218 22-200

0

200

400

600

Ion

o s

lop

e (m

mk

m)

20 202 204 206 208 21 212 214 216 218 2240

50

60

70

80

Ele

vati

on

(d

eg)

Time (hour of 11202003)

a)

b)

c)

Fig 9 Maximum ionospheric gradient observed at high elevation

a) dual-frequency estimates of slant ionospheric delay b) spatial rate of

change and c) elevation from JPL-processed CORS data vs UT hour for

receivers ZOB1 and GARF tracking SVN 38 at high elevation

20 202 204 206 208 21 212 214 216 218 22-50

0

50

100

150

200

250

300

350

400

450

Time (hour of 11202003)

Ion

o S

lop

e (m

mk

m)

ZOB1 and GARF Slopes Comparison SV38

DF Slope

L1 Slope

Fig 10 Spatial rate of change of ionospheric delay over time between

two stations in OhioMichigan cluster viewing SVN 38 at high elevation

from rIDF (blue) and rICMC (red)

207 208 209 21 211 212 213 2140

5

10

15

20

25

30

35

40

45

50Iono Delay for Selected Stations SV 26 Elevation100689ordm - 12078ordm

Time(Hour of 11202003)

Sla

nt Io

no D

elay

(m

)

FREO

WOOS

LSBN

GARFGUST

Fig 11 Dual-frequency estimates of slant ionospheric delay from JPL-processed CORS data vs UT hour for select stations in the Ohio

Michigan cluster tracking SVN 26 at low elevation

208 209 21 211 212 213 214-50

0

50

100

150

200

250

300

350

400

Time (hour of 11202003)

Ion

o S

lop

e (m

mk

m)

WOOS and GARF Slopes Comparison SV26

DF Slope

L1 Slope

Fig 12 Spatial rate of change of ionospheric delay over time between

two stations inOhioMichigan cluster viewing SVN26at lowelevation as

from rIDF (blue) and rICMC (green)

1148 DATTA-BARUA ETAL

2000ndash2005 during the maximum and waning phases of the mostrecent solar cycle

Figure 13 summarizes the results of this data analysis process bypopulating the threat space with cases observed in CORS data fromthe storms examined In Fig 13a the instances of anomalousgradients are plotted as a function of slope (mm=km) on thehorizontal axis and ground speed (m=s) on the vertical axis Thepoints that were identified in dual-frequency data and subsequentlyverified with single-frequency L1 code-minus-carrier data aremarked with diamonds The points that were estimated from therICMC directly are shown as triangles These same points are plottedin Fig 13b as a function of elevation angle (degrees) on the horizontalaxis and slope (mm=km) on the vertical axis

Note that width estimates do not appear in this figure Thewidth ofthe TEC rampwas generally difficult to assess from the available datagiven the typical 50ndash100 km separation of CORS reference stationsIn addition the integrity results (ie the threat model impacts onLAAS users) were not found to be sensitive to this parameter [27]

Empirical bounds are fitted to the points shown in Fig 13 in orderto limit the threat space to the maximum gradients discovered andvalidated These bounds are marked with solid lines in Fig 13Figure 13a shows that for events moving at speeds less than 90 mswith respect to the ground a maximum slope of 150 mmkm boundsthe events observed Figure 13b shows the upper bound on themaximum gradient of this threat model as a function of satelliteelevation angle for speeds greater than 90 ms a slope of375 mm=km at low elevation increasing linearly at a rate of 1 mmkm from 15 to 65 deg elevation to reach 425 mm=km at highelevation For ground speeds below 90 ms the maximum slope is150mmkm independent of elevation These bounds slightly exceedthe largest gradients validated from the data analysis due to marginadded to account for measurement error In addition to the plottedmaximum gradient and speed bounds width (distance between highand low delay regions) is delimited to be between 25 and 200 km andtotal slant differential delay up to 50 m [2236]

Figure 13a shows a maximum estimated ionospheric front speedof about 600 m=s with respect to the ground and the speed was

limited to be nomore than 750 m=s This bound is based on a numberof considerations First in practice the absolute speed is of secondaryimportance to the front speedwith respect to the aircraft andLGF ray-paths (or effectively their IPPs) The relative motion permitsdetectability in fact the estimates of speed were very sensitive to thisrelative motion There is a real (albeit unlikely) possibility that IPPspeed might cancel out the actual ionospheric front speed relative tothe ground and thus prevent time-based LGF monitoring fromdetecting very large gradients for several minutes This possibilitysignificantly increases the maximum possible ionosphere-inducederror for LAAS users beyond what was initially projected in workthat did not consider this masking possibility (eg [37]) It alsoaffects the ionospheric anomaly simulation algorithms described in[2338] as the method for selecting the worst possible ionosphericfront from the point of view of a given satellite is now dictated by thetheoretical speed of the resulting IPP

The threat space shown in Fig 13 is used to represent whatthe ionosphere is potentially capable of during rare extreme-ionospheric-storm conditions based on the validated events observedin the ionospheric data analysis described in Sec V However inadopting this threat model for the present wemust recognize that thevalidated events that have occurred to date are few in number sincethey are driven by the most extreme storms that have impacted theAmerican sector and cover a limited geographical area withinCONUS due to constraints imposed by the CORS receiver sites andequipage It is possible that localized events severe enough topotentially threaten LAAS users have occurred in CONUS but arenot included in the data used to construct the threat space Thereforewhile this threat space is deemed final for the purposes of CAT ILAAS certification [39] the anomaly mitigation algorithms haveadjustable parameters in case future ionospheric storms or dataanalyses reveal gradients that fall outside this threat space Anotherpossibility is that better understanding of how the ionospherebehaves during storm events will allow us to reduce the conservatismin the model as well as develop a more physically accuratedescription of how ionospheric anomalies propagate through LGFand user observations

Users can be protected from these situations by inflating theprotection bound that is broadcast to exclude geometries that wouldlead to intolerably large positioning errors [2338] Even thoughthere is no evidence of such large gradients on nominal days withoutthe ability to detect ionospheric storms in real time that is possessedby WAAS LAAS must assume that the ionosphere may be in adisturbed state at all times As a result geometries that would beunsafe during a storm are disallowed even though severe storms arevery unlikely to be present thus sacrificing precision approachavailability One method for excluding geometries that achieves anacceptable level of availability is described in [23]

VII Conclusions

This paper represents the culmination of several years ofteamwork in conducting as comprehensive a search as possible forpast extreme ionospheric events inCONUS thatwould impact LAASuser safety We have identified the ionospheric threat to users ofLAAS parameterized a model to represent it and applied a two-phase method of ionospheric spatial-gradient analysis and validationto quantify the magnitude of these threats This method consists ofautomatic processing of dual-frequency GPS carrier-phase measure-ments of ionospheric delay combined with manual comparison tosingle-frequency code-carrier divergence measurements to searchfor an upper bound on possible anomalous ionospheric gradientsThe final threat space of the model is ultimately driven by the largestof these points found but the parameters of this model have gonethrough a number of revisions in magnitude throughout this processThese values represent our best-validated observations during whatare recognized within the ionospheric community as the mostextreme geomagnetic storms during and following the most recentsolar maximum

The most time-consuming aspect of ionospheric anomaly dataanalysis is manual review of the apparent anomalies output by the

Fig 13 Ionospheric gradient threat space anomalous ionospheric

gradients observed plotted in threat model space as a function of a) slope

and ground speed and b) elevation and slope

DATTA-BARUA ETAL 1149

automated-search software In themajority of cases it was evident tohuman eyes that receiver or postprocessing errors created an apparentionospheric gradient that was not real and these observations wereset aside However a significant number of events that could not bediscounted remained andwere subjected tomore detailed analysis byteam consensus

As a result of this work we have validated low-elevation spatialgradients in the ionospheric delay at L1 as high as 360 and413 mm=km at high elevation These observations were made withCORS network stations in northern Ohio during the 20 Nov 2003ionospheric storm Station redundancy rules out the possibility ofthese gradients being generated by faulty receivers or significanterrors in receiver bias estimation Simultaneous observations onother satellites at similar elevations were also unusually highTherefore we conclude that the largest gradients observed herewerenot due to nonionospheric causes

The impact of these gradients on WAAS precision approachintegrity is negligible due to the implementation of the WAASextreme storm detector (however WAAS precision approachavailability is dramatically affected when severe storms occur)Mitigating the safety threats posed by ionospheric storms to users ofLAAS is achieved by disallowing specific user satellite geometriesduring precision approach at the cost of somewhat reducing LAASCAT 1 availability

Acknowledgments

The authors thank the FAA LAAS Program Office for theirsupport in the completion of this effort We would also like to thankspecific individuals who helped us in this work including ToddWalter Juan Blanch and Shankar Ramakrishnan at Stanford JohnWarburton and Tom Dehel of the Federal Aviation AdministrationWilliam J Hughes Technical Center and Attila Komjathy of theNASA Jet Propulsion Laboratory

References

[1] Enge P ldquoLocal AreaAugmentation ofGPS for the PrecisionApproachof Aircraftrdquo Proceedings of the IEEE Vol 87 No 1 Jan 1999pp 111ndash132doi1011095736345

[2] Enge P Walter T Pullen S Kee C Chao Y C and Tsai Y JldquoWide Area Augmentation of the Global Positioning SystemrdquoProceedings of the IEEE Vol 84 No 8 Aug 1996 pp 1063ndash1088doi1011095533954

[3] Misra P and Enge P Global Positioning System Signals

Measurement and Performance 2nd ed GangandashJamuna LincolnMA 2006

[4] Tascione T F Introduction to the Space Environment KriegerMalabar FL 1994

[5] Klobuchar J ldquoIonospheric Effects on GPSrdquo Global Positioning

System Theory and Applications edited by B W Parkinson and J JSpilker Vol 1 Progress in Astronautics and Aeronautics AIAAWashington DC 1996 pp 485ndash515

[6] Datta-Barua S Mannucci A J Walter T and Enge P ldquoAltitudinalVariation of Midlatitude Localized TEC Enhancement from Ground-and Space-BasedMeasurementsrdquo Space Weather Vol 6 No S10D062008doi1010292008SW000396

[7] Roble R G and Ridley E C ldquoThermospherendashIonospherendashMesosphere Electrodynamics General Circulation Model (TIME-GCM) Equinox Solar Cycle Minimum Simulations (30ndash500 km)rdquoGeophysical Research Letters Vol 21 No 6 1994 pp 417ndash420doi10102993GL03391

[8] Crowley G et al ldquoGlobal ThermospherendashIonosphere Response toOnset of 20 November 2003Magnetic Stormrdquo Journal of GeophysicalResearch Vol 111 No A10S18 2006doi1010292005JA011518

[9] Huba J D Joyce G and Fedder J A ldquoSami2 is Another Model ofthe Ionosphere (SAMI2) A New Low-Latitude Ionosphere ModelrdquoJournal of Geophysical Research Vol 105 No A10 2000 pp 23035ndash23053doi1010292000JA000035

[10] Bilitza D ldquoInternational Reference Ionosphere 2000rdquo Radio ScienceVol 36 No 2 MarchndashApril 2001 pp 261ndash275

doi1010292000RS002432[11] PiXWangC Hajj GA RosenGWilson BD andBaileyG J

ldquoEstimation of ExB Drift Using a Global Assimilative IonosphericModel An Observation System Simulation Experimentrdquo Journal of

Geophysical Research Vol 108(A2) No 1075 2003doi1010292001JA009235

[12] Schunk R et al ldquoGlobal Assimilation of Ionospheric Measurements(GAIM)rdquo Radio Science Vol 39 No RS1S02 2004doi1010292002RS002794

[13] Bust G S et al ldquoFour-Dimensional GPS Imaging of Space WeatherStormsrdquo Space Weather Vol 5 No S02003 2007doi1010292006SW000237

[14] Klobuchar J A Doherty P H and El-Arini M B ldquoPotentialIonospheric Limitations to Wide-Area Differential GPSrdquo Proceedingsof the 16th International Technical Meeting of the Satellite Division of

The Institute Of Navigation (IONGPS 1993) Salt Lake City UT 1993pp 1245ndash1254

[15] Klobuchar J A and Kunches J ldquoEye on the Ionosphere The SpatialVariability of Ionospheric Range Delayrdquo GPS Solutions Vol 3 No 32000 pp 70ndash74doi101007PL00012808

[16] Walter T et al ldquoRobust Detection of Ionospheric IrregularitiesrdquoProceedings of IONGPS 2000 httpwaasstanfordedu~wwupapersgpsPDFtoddion00pdf Salt Lake City UT 2000 pp 209ndash218

[17] Christie J R I Ko P-Y Hansen A Dai D Pullen S Pervan Band Parkinson B ldquoThe Effects of Local Ionospheric Decorrelation onLAAS Theory and Experimental Resultsrdquo Proceedings of the Instituteof Navigation National Technical Meeting 1999 pp 769ndash777

[18] ldquoMinimum Operational Performance Standards for GPS Local AreaAugmentation System Airborne Equipmentrdquo Radio TechnicalCommission for Aeronautics Washington DC Publ DO-253C2008

[19] Lee J Pullen S Datta-Barua S and Enge P ldquoAssessment ofNominal Ionosphere Spatial Decorrelation for GPS-Based AircraftLanding Systemsrdquo Journal of Aircraft Vol 44 No 5 2007 pp 1662ndash1669doi102514128199

[20] Datta-Barua SWalter T Pullen S LuoM Blanch J and Enge PldquoUsing WAAS Ionospheric Data to Estimate LAAS Short BaselineGradientsrdquo Proceedings of the Institute of Navigation National

Technical Meeting 2002 pp 523ndash530[21] Luo M Pullen S Akos D Xie G Datta-Barua S Walter T and

Enge P ldquoAssessment of Ionospheric Impact on LAAS Using WAASSupertruth Datardquo Proceedings of the ION 58th Annual Meeting and

CIGTF 21st Guidance Test Symposium 2002 pp 175ndash186[22] Ene A Qiu D Luo M Pullen S and Enge P ldquoAComprehensive

Ionosphere Storm Data Analysis Method to Support LAAS ThreatModel Developmentrdquo Proceedings of the Institute of Navigation

National Technical Meeting 2005 pp 110ndash130[23] Ramakrishnan S Lee J Pullen S andEnge P ldquoTargetedEphemeris

Decorrelation Parameter Inflation for Improved LAAS AvailabilityDuring Severe Ionosphere Anomaliesrdquo Proceedings of the 2008

National TechnicalMeeting of the Institute of Navigation pp 354ndash366[24] Komjathy A L Sparks and A J Mannucci ldquoGenerating High

Precision Ionospheric Ground-Truth Measurementsrdquo US PatentNo 7289061 B2 filed 30 Oct 2007

[25] Datta-Barua S ldquoIonospheric Threats to the Integrity of Airborne GPSUsersrdquo PhD Dissertation Stanford Univ Palo Alto CA 2008

[26] Luo M Pullen S Dennis J Konno H Xie G Walter T Enge PDatta-Barua S and Dehel T ldquoLAAS Ionosphere Spatial GradientThreat Model and Impact of LGF and Airborne MonitoringrdquoProceedings of the 16th International TechnicalMeeting of the Satellite

Division of the Institute of Navigation IONGPSGNSS 2003 pp 2255ndash2274

[27] Luo M Pullen S Walter T and Enge P ldquoIonosphere SpatialGradient Threat for LAAS Mitigation and Tolerable Threat SpacerdquoProceedings of the 2004 National Technical Meeting of the Institute of

Navigation pp 490ndash501[28] National Geodetic Survey CORS Team ldquoCORS Continuously

Operating Reference Stationsrdquo httpwwwngsnoaagovCORS[retrieved 6 Jan 2009]

[29] National Geophysical Data Center ldquoSpace Physics Interactive DataResourcerdquo 2005 httpspidrngdcnoaagovspidr [retrieved1 March 2005]

[30] Sugiura M and Kamei T ldquoEquatorial Dst Index 1957ndash1986rdquoInternational Association of Geomagnetism and Aeronomy Vol 401991 pp 7ndash14

[31] Menvielle M and Berthelier A ldquoThe K-Derived Planetary IndicesDescription and Availabilityrdquo Reviews of Geophysics Vol 29 No 3

1150 DATTA-BARUA ETAL

delay difference per km of user-LGF separation distance [19] TheLAAS integrity bound calculation includes this term and the needfor a model to quantify anomalous behavior that might escapedetection was not thought to be needed for baselines of less than50 km

However Datta-Barua et al [20] first investigated dual-frequencyGPS network data during an ionospheric storm in 2000 thatdemonstrated that the differential delay could possibly be hundredsof mm=km Luo et al [21] posited a model for the user-LGF-ionospheric threat scenario and demonstrated that differential delaysof hundreds of mm at L1 frequency per km could pose unacceptablyhazardous conditions to a LAAS user under certain geometries andrelative aircraft motions The observation and impact of such highspatial rates of change in delay over short baselines invalidated theprevious belief that the broadcast vig would bound anomalousionosphere Since boundingwas not possible an ionospheric spatial-gradient threat model for LAAS representing possible anomaliesthat could not be bounded by vig needed to be developed andmitigated to the degree that it threatened LAAS users Developingthis model took several years involved several researchers andrequired several iterations before culminating in the work presentedhere

In this paper we review the methodology of this data analysis andexplain the current ionospheric threat model for CONUS whichmust be mitigated by the LGF to meet the integrity requirements forCAT I precision approaches [2223] Sec II provides the motivationfor modeling anomalous ionospheric behavior namely the dis-covery that the differential error over baselines of a few kilometerscan be hundreds ofmm=km at L1 In Sec III we develop a model forthese spatio-temporal variations and define the fundamental param-eters that characterize this model Sec IV introduces the dual-frequency GPS data sets that are used in this work and describes theuses and relative strengths of each The algorithm for automatedanalysis and procedures for manual verification are investigated inSec V which is the most time-intensive part of our study Thefinalized threat space of this model incorporating the extremeparameter values are summarized in Fig 13 of Sec VI Sec VIIcontains our concluding remarks

II Severe Ionospheric GradientsDiscovered in CONUS

The discovery of spatial rates of change shown in this section thatare more than 2 orders of magnitude higher than nominal

decorrelation rates motivates the need for an ionospheric threatmodel because such gradients could never be bounded by a practicalbroadcast sigma value Measurements of ionospheric delay or TECcan be made with dual-frequency ranging receivers which takeadvantage of the dispersive nature of the ionosphere as given byEq (2) For dual-frequency GPS receivers the observables at the L1frequency are given by Eq (1) and the observables at the secondfrequency (in this paper f2 122760 MHz) are

2 rki bi Bk Tki Iki IFBi kgd 22 rki bi Bk Tki Iki IFBi kgd 2N2 2

f21

f22

(3)

The range r clock biases b and B and tropospheric error T areidentical to those in Eq (1) Similar noise terms and integerambiguitiesN exist at the L2 frequency The ionospheric delay at theL2 frequency is proportional to the delay I at the L1 frequency by thesquared frequency ratio Additionally receiver hardware inter-frequency biases (IFB) and satellitegd affect the estimate of delay

The slant ionospheric delay at L1 can be computed from the GPScode and carrier observables at L1 and L2 frequencies given inEqs (1) and (3) in three ways

I 2 1 1

I 1 2 1

ICMC 1 1

2(4)

The code estimate I is noisy because but has no integerambiguity The carrier estimate I is low-noise but contains integerambiguities for both L1 and L2 The single-frequency carrier minuscode (CMC) or code-carrier divergence estimate ICMC is robust tothe fragility of L2 codeless and semicodeless tracking loops butcontains both the L1 integer ambiguity and noise In the dual-frequency estimates I and I the receiver and satellite hardwarebiases (IFB and gd) must be removed

For this initial study high-quality ionospheric data for theCONUSregion from the FAA WAAS network were used WAAS had 25ground stations over the United States during the period underinvestigation Each ground station has three L1ndashL2 dual-frequencyreceivers allowing for a directmeasurement of the ionospheric delayThe raw receiver carrier-phase data is leveled to the code-phase datain postprocessing to remove integer ambiguitiesN1 andN2 Satellitebias gd and receiver interfrequency bias IFB must be estimated and

Fig 3 Map of CORS station (dots) and WAAS reference station (diamonds) locations

DATTA-BARUA ETAL 1143

removed separately [24] WAAS receiver redundancy allows forvoting to remove possible artifacts due to problems on individualreceivers These data are reliable but geographically sparse relative tothe LAAS service area as the closest WAAS reference stations are255 km apart These locations are denoted with diamonds in Fig 3

To estimate the spatial rate of change possible in a LAASconfiguration the difference in delay along two lines of sight (LOSs)with a distance between them that is characteristic of LAAS must bemeasured One technique that is especially useful for analyzingdifferential delayswhen receivers are sparsely distributed is the time-stepmethod in which the IPP traversal distance over time substitutesfor the LAAS ground stationaircraft separation [19]

WAAS data was used to identify times and regions exhibiting highrates of change as computed by the time-step method during theionospheric storm of 6 April 2000 [20] During this storm a largeSED passed over CONUS and its duskward edge caused highdifferential delays Figure 4 shows the slant ionospheric delay inmeters at each of the threeWAAS receivers at ZDCas they track SVN40 The horizontal axis below each subplot marks the elapsed time inseconds For each receiver I (red) I (blue) and ICMC (green) areplotted Both I and ICMC are recentered to the time-averaged codemeasurement of the delay I to account for the ambiguous integernumber of cycles

Over a time interval of 110 s demarcated with vertical lines onFig 4 the receiver tracked through a drop in the slant ionosphericdelay of about 8 m There were no cycle slips (discontinuities in themeasured ionospheric delay due to temporary receiver trackingproblems) for any of the receivers during the time interval inquestion The identical time evolution shown by all three receivers onall three forms of estimated delay indicates that the observedanomaly was not due to receiver bias

A similar event for an independent LOS an hour and a half earlieron the same day further confirms the ZDC-SVN 40 anomaly Thereceiver measurements of the ionosphere between theWAAS stationZBWand SVN 24 show that while station ZBW was tracking SVN24 during a span of 150 s the slant ionospheric delay on this LOSdropped by about 10 m Given SVN 24rsquos position in the sky duringthis time interval the IPP of the ray-path traveled about 15 km Thefact that these observations represent two independent LOSsindicates that neither biases at an individual receiver or satellite norcycle slips contributed to the high apparent decorrelation Thereforethe anomalous events that affected LOS ZDC-SVN 40 and LOSZBW-SVN 24 were both records of actual ionospheric events

However the gradients they imply (8m at L1 over 7 km separationand 10 m over 15 km) may be artificially inflated for the followingreason The anomaly at station ZBWas it tracked SVN 24 precededthe anomaly along LOS ZDC-SVN 40 by 15 h Ionospheric TECmaps for this storm period shown in the Appendix to [25] roughlyindicate that the storm front whose boundary runs roughly east-westrecedes southward for the duration Having first been traversed by anIPP from station ZBW it progresses south to be traversed by an IPPfrom ZDC 15 h later Based on this observation to first order its

velocity vfront 110 m=s southward The IPP associated with ZDC-SVN 40 was moving primarily northward with ground speedvIPP 7 km=110 s 63 m=s respectively The velocity of thestorm front is nonnegligible compared with the IPP velocity Sincethe velocities were directed roughly opposite to each other theadditive effect of the relative velocities would make the spatialgradient in the ionosphere appear to be a factor of two ormoreworsethan it actually was Instead of a 7 kmwidth thewidthw of the stormfront was more likely

w vfront vIPP t 110 63 110 19 030 m (5)

This means that the purely spatial gradient of the ionosphere wasmore likely 8 m of slant delay difference over a 19 km range w asshown in Eq (5) giving a 420 mm=km spatial rate of change of slantdelay

From this event we learned about the potential existence of spatialrates of change in ionospheric delay with magnitudes that could notbe bounded by the LAAS nominal broadcast correction We alsoobserved that estimating the spatial rate of change is complicated bythe mixing of spatial and temporal variations in the ionospherethrough SED motion as well as the motion of LOSs relative to theionospheric structure

III Ionospheric Threat Model Parameterization

The discovery of high spatial gradients at the edges of ionosphericSEDs combined with relative motions of the ionosphere and LOSswas used to develop an ionospheric threat model for identifyingpossible threats to LAAS users In this section we define a model foran ionospheric wave front and associated model parameters

Based on the findings in Sec II the ionospheric threat is modeledas a spatially linear semi-infinite wave front with constantpropagation speed Figure 5 illustrates this model The model isparameterized by the slope g of the ramp and its widthw the productof which yields a maximum ionospheric delay D and the groundspeed v of wave front The observations in Fig 4 of a constant highdelay a period of decline at a fairly constant rate and a period ofconstant low delay supports the piecewise linear model A higherorder model may fit the actual data better However simplicity in themathematical form of an ionospheric threat model enables easierassessment of impact on users through LAAS simulation tools andidentification of mitigation strategies

Themaximum ionospheric delay difference is upper-bounded by amaximum value which was set based on observations Slope andwidth values whose product creates a maximum delay differenceabove the bound on D are not part of the threat model and arediscarded This limit is conservative because it allows for possibleionospheric configurations that may be physically impossible but itkeeps the simulation search space tractable In any case as Luo et al[26] showed the extreme regions of the search space will be easilydetected by the LGF within the time to alarm when the ionosphericfront is verywide and thus sweeps across thefield of viewof the LGFFortunately the impact of ionospheric wave fronts on LAAS is notvery sensitive to width thus the choice was made to include a rangeof possible frontwidths from25 to 200 km in the threatmodel Largerwidths would not produce gradients sufficiently sharp to bethreatening and smaller widths would not cause much difference inLAAS impact simulation results [27] Thus data analysis proceduresare devoted to estimating the slopeg and speed v of ionosphericwavefront (Sec V)

Fig 4 Slant ionospheric delay in m between WAAS receivers at

Washington DC and GPS satellite 40 on 6 April 2000

Max Iono delay

D = w g

Front Speed (v )

Nominal IonoWidth (w)

Iono Front

Slope (g)

Fig 5 Ionospheric wave-front model

1144 DATTA-BARUA ETAL

As noted above the gradient is assumed to represent a linearchange in slant ionospheric delay between maximum and minimumdelays As a result the bounding slant gradients are also (at leastpotentially) functions of satellite elevation

To recap this threat model assumes severe ionospheric spatialgradients take the form of straight horizontally semi-infinite wavefronts of linearly varying TEC that move at constant speed anddirectionwith respect to the ground In addition the slope of the rampitself is not time-varying These are simplifications of reality but arereasonable for the duration of an aircraft approach These assump-tions are adequate because deviations from them do not materiallychange the final result except if most-evil variations (compoundingthe worst-case upon the worst-case) are allowed

The ionospheric threat model does not include other parametersthat are necessary for simulating and assessing impact on LAAS-equipped aircraft such as the orientation of the ionospheric wavefront with respect to the aircraft and runway the horizontal approachspeed of the aircraft the approach direction of the ionospheric frontthe initial position of the ionospheric front with respect to the aircraftat the start of the simulation and the velocities of the IPPs which aredriven by satellite and receiver motion Figure 1 illustrates anexample of one possible configuration of the ionospheric threatmodeluserrunwaysatellite scenario

IV Data

The remaining sections of this paper are concerned withidentifying theworst observed values of the ionospheric threatmodelparameters of slope and speed at different ranges of elevation anglesExisting ionospheric models do not capture anomalous behaviorsuch as that observed on 6 April 2000 so our threat-space devel-opment for the ionosphere is data-driven Precise estimates ofionospheric delays can be obtained using dual-frequency GPSmeasurements from networks of stations and sophisticated post-processing algorithms

The US National Geodetic Survey makes dual-frequency GPSdata publicly available in RINEX format from a network ofcontinuously operating reference stations (CORS) [28] CORS hashundreds of stations in CONUS (sites as of 29 Oct 2003 are shownin Fig 3) and permits ionospheric measurement comparisons acrossbaselines of tens of kilometers Before analysis geographicalclusters of the highest-density receiver regions in the United Statesare targeted to provide the best possible spatial resolution Eightclusters overCONUSof around 21 square degrees of average area aredefined and boxed in Fig 3 Each of these clusters contains 10ndash30receivers with baseline separations of 15ndash200 km Most of the dataanalysis is performed for stations inside these clusters In some casesadditional stations adjacent to these clusters are also used to bettercapture ionospheric wave-front behavior that extended beyondcluster boundaries

The dates fromwhich data were collected and analyzed are shownin Table 1 along with the publicly available indices of globalgeomagnetic activity planetary K (Kp) and disturbance storm time(Dst) and geomagnetic storm class (G-class) and WAAS coverageParticular geographic regions of focus if applicable are shown

The values of Kp Dst G-class and WAAS coverage are used tohelp target days onwhich anomalous behavior in CONUSwas likelyto have occurred Because the ionosphere is coupled to themagnetosphere which is in turn driven by the solar wind measuresof geomagnetic activity provide information about the ionosphere byproxy The Dst index measures activity in the worldwide geomag-netic field [2930] with negative deviations indicating ionosphericcurrents and activity Kp is a three-hour composite index based ongeomagnetic activity measured at several midlatitude stationsprimarily located in the northern hemisphere [3132] Kp ranges inthirds of an index unit from 0 (no activity) to 9 (extreme activity) TheSpace Weather Prediction Center of the National Oceanic andAtmospheric Administration has developed a geomagnetic stormscale and associated descriptors for use by both the technicalcommunity and the general public [33] The scale varies from G1throughG5 and the storm classes associated with each level in orderof increasing intensity are minor moderate strong severe andextreme

The percentage of WAAS coverage indicates the degree to whichthese storms introduced nonlinear spatial variations over CONUS(viewed over separations of hundreds of km between WAASreference stations) limiting WAAS availability for precision ap-proach While the other measures shown in Table 1 are globalWAAS coverage is a daily summary value that indicates how muchimpact each storm had in the American sector where the CORSstations are sited For the dates occurring before WAAS initialoperating capability (IOC) official coverage percentages do not exist

The data collected from the CORS network have been assimilatedand had biases removed by the use of the global ionosphericmapping(GIM) software atNASArsquos Jet PropulsionLaboratory (JPL)With theadditional use of the publicly available International GNSS Servicestation data worldwide GIM estimates and removes satellite andreceiver interfrequency biases to provide high-precision ionosphericmeasurements The dual-frequency ionospheric observables IDF arecomputed by leveling the phase estimates I using the codeestimates I The processing with a leveling function f is describedin detail in [34] The resulting data are provided at 30 s intervals

IDF fI I (6)

CORS ionospheric estimates are processed in a similar manner tothose ofWAAS (see Sec II) but the lack ofmultiple receivers at eachsite means that anomalies due to receiver glitches cannot be votedout In addition the quality of CORS receiver equipage and siting ismuch more variable than that of WAAS reference stations thustypical CORS receiver measurement errors are several times higherthan that of WAAS receivers The screening algorithms used at JPLare based on an error detection threshold that was set to allow moredata points and minimize the risk of discarding true ionosphericevents

V Data Analysis Procedure

The objective of this section is to produce the LAAS threat spaceon parameters g and v using the data from closely-spaced CORSstations described above The procedure for processing CORS

Table 1 Ionospheric storm dates and conditions investigated

Day (UT mmddyy) Kp Dst Geomagneticstorm class

WAAS coverage Focus region

040600 83 287 Severe None (pre-IOC) NE corridor040700 87 288 Extreme None (pre-IOC) NE corridor071500 90 289 Extreme None (pre-IOC) mdashmdash

071600 77 301 Strong None (pre-IOC) mdashmdash

090702 73 163 Strong None (pre-IOC) mdashmdash

102903 90 345 Extreme 0 mdashmdash

103003 90 401 Extreme 0 TXndashOKndashLAndashAR103103 83 320 Severe 0 FLndashGA112003 87 472 Extreme 0 OHndashMI071704 60 80 Moderate 688 TXndashOKndashLAndashAR

DATTA-BARUA ETAL 1145

receiver data identifying anomalously high spatial rates of changevalidating anomalies that are due to ionospheric events andestimating parameters in terms of the threatmodel of Sec III is shownin Fig 6 The starting point is raw L1 and L2 measurements fromselected clusters of CORS stations An automated algorithm flowingdown the left-hand side of the figure involves using the JPL-processed CORS-based estimates of ionospheric delay to computeapparent spatial gradients and front speeds and screen them forobvious receiver or data-processing errors The remaining points arecompared manually (right-hand side of Fig 6) to estimates derivedfrom raw CORS data The following sections trace through themethodology outlined in the flowchart in Fig 6

A Ionospheric Spatial-Gradient Estimation

1 Station Pair Method

Our approach involves considering selected pairs of CORSstations in the JPL-processed database as though they represent anLGF-user receiver pair (with a static user position) This techniquecalled the station pair method is illustrated in Fig 7 For each epocht the delays at each of two stations i and j viewing the same satellite kare differenced The slope of ionospheric delayrI between all pairsof receivers looking at each satellite is estimated by dividing thedifferential slant delay by the baseline distance between the tworeceivers

rIt jIki t Ikj tjkxi xjk

(7)

The station pair method unlike the time-step method (Sec II)does not artificially inflate ionospheric gradients due to relativemotions of IPPs and the ionosphere [19]

The dual-frequency data in each CORS cluster are separated intofive elevation bins 0ndash12 12ndash20 20ndash30 30ndash45 and 45ndash90 deg Foreach LOS elevation range receivers in each cluster are grouped intopairs and IDF is used for I in Eq (7) to give a dual-frequencyestimate of the gradient rIDF

2 Automated Screening

AfterrIDF is computed an automated screening process (bottom-left corner of Fig 6) isolates the most credible events above a chosenthreshold (100 mm=km) and eliminates those gradients that areapparently due to nonionospheric causes Cases for which tworeceivers are effectively collocated (less than 100m apart) or the slantgradient rIDF does not vary in time are attributed to an interreceiverbias and eliminated Cases for which ionospheric delay measure-ments from one receiver do not vary in time are attributed to a faultyreceiver and eliminated

Variations greater than 15 mm=s ionospheric delay rate of changeare flagged for possible ionospheric activity The 15 mm=s thresholdis set to be an order of magnitude lower than the largest time rate ofchange observed for the 29 Oct 2003 storm day [35] in order toallow significant rapid changes in the delay value to pass screening aspossible ionospheric events The advantage of this threshold is thatreal ionospheric events are less likely to be eliminated as falsenegatives The tradeoff is that false alarms occur when the datacontain cycle slips causing the delay value to jump suddenlybetween two successive measurement points These cases have to beeliminated manually from the screening results

3 Manual Validation

The plausible ionospheric events that survive the computer-automated screening process are analyzed manually For each ofthese events visual analysis determineswhether themagnitude of thegradients together with the length of time that these gradientsmanifest themselves look reasonable or aremore likely to be artifactsof faulty receiver tracking

The first step in verifying an event is by comparison againstsimultaneous observations of slant delay from other nearby receiversin the same cluster This can be thought of as cluster-wide voting thatoperates in a similar manner to the voting among colocated receiversat each WAAS reference station If other nearby CORS receiversexhibit similar ionospheric delay patterns to the pair that shows themost-extreme spatial decorrelation the event is very probably realand is not limited to a single receiver fault On the other hand if theobservations were influenced by receiver artifacts in the data it isunlikely that amajority of the receivers in the cluster contain the sameor similar artifacts

To support this comparison plots are constructed that extendacross the entire period of time for which data was extracted so thatthe receiver behavior can be analyzed both in the presence ofdisturbed ionosphere and also during calm ionospheric conditions oneither side of the disturbance While the electron content within theionosphere is expected to vary wildly during the actual storm delaymeasurements from the different receivers within a CORS cluster areexpected to converge to similar values at times of nominalionospheric activity Quiet periods of ionospheric behavior help toestablish a baseline for the affected receiver cluster and give anindication of any possible interreceiver biases

The next step in validating extreme gradients (top-right corner ofFig 6) is to estimate approximate wave-front parameters from rawL1 code-minus-carrier measurements ICMC and compare them to theobservations from the JPL-processed dual-frequency data IDF TheL1-only estimates of these slopesrICMC given by using ICMC for theslant delay I in Eq (7) provide a picture of gradients that is notsubject to fragile L2 tracking loops The L1-only estimates are notexact however because the integer ambiguity N1 has not beeneliminated

Two methods are used to remove the integer ambiguity for high-elevation and low-elevation LOSs respectively For high elevationthe postprocessed L1L2 estimate of absolute differential sloperIDFat a timewell before or after thewave front passes is used to initializethe single-frequency ionospheric gradient estimates rICMC A pointin time is picked at which the measurements rICMC are aligned torIDF by subtracting a constant bias from all the measurement of oneof the receivers equalizing the value that they measure at the choseninstant Since the differential delay between a pair of receivers isobserved this is equivalent to choosing a reference zero-slope point

L1 code and carrier

L1 code and carrier L2 carrier

ldquoRawrdquo IGSCORS Data

JPL Ionosphere ldquoTruthrdquo Processing

Ionosphere Delay Estimates

Find Maximum Apparent Gradients

(Station Pair Method)

Screening Process (Automated)

Database of Extreme Gradients Erroneous Receiver

Steps and L1L2 Biases Removed Investigate

Remaining Points

Remove ldquoQuestionablerdquoObservations

Estimate Gradients from L1 code-minus-carrier

Output Database of Maximum Gradients

Compare L1L2 and L1 CMC Gradient Obs

ldquoValidatedrdquoMax Gradient

Database

Fig 6 Ionospheric anomaly data analysis procedure

Fig 7 Station pair method Not to scale

1146 DATTA-BARUA ETAL

in rICMC at a particular instant in time and then expressing all otherslope measurements relative to it Thus L1 CMC ionosphericgradient estimates form lower bounds on the true gradient but theseestimates are accurate enough that they can be used to validate theL1L2 observations

For low-elevation satellites the track may not be long enough tosample through quiet ionosphere In this case the single-frequencygradient estimate is leveled to the mean of the JPL-processed dual-frequency estimate rIDF as shown in Eq (8)

r ~ICMCt rICMCt rICMC rIDF (8)

Anomalous ionospheric events not eliminated by this final manual

comparison to single-frequency estimates r ~ICMC of the spatialvariation are deemed to be validated measurements rIDFValidated andare used to generate bounds on the threat model parameter g

gmaxtrIDFValidated (9)

B Ionospheric Front Speed Estimation

Observations of severe gradients that are validated as described inSec VA are studied inmore detail to characterize them in the contextof the ionospheric threatmodel geometry The automated code used athree-station-based trigonometric algorithm to estimate the speedand direction of wave-front propagation with respect to the ground(see [22] for details) The travel time of the frontwhich swept a pair ofstations is measured to compute the speed and the third station isused to observe the front direction This algorithm proved to be verysensitive to measurement errors and limitations of the threat model(in particular the assumption that the wave front is a straight line)when extrapolated to cover three (instead of two) stations

To overcome the weakness of the automated ionospheric frontspeed computation method a manual two-station-based method isused that takes wave-front orientation (estimated manually based onmany stations) as an input so that only speed needs to be solved forFirst we form groups that typically include four to eight nearbyCORS receiverswhich show similar ionospheric delay patterns to thesatellite-receiver pair from which anomalous gradients wereobserved and verified By observing the time history of ionosphericslant delays from the stations observing the same satellite the peakdelay times tpeaki at which the slant delay of the station i reached thelocal maximum value are determined Because ionospheric delays ofthese nearby stations exhibit similar patterns the order in which thestations were affected by the ionospheric front can be deduced fromtpeaki This order and the locations of the affected stations make itpossible to approximately determine the forward propagationdirection of the front Since it was assumed that the wave front is astraight line and moves with constant speed which is not true inreality the sequence and tpeaki must be carefully adjusted manuallyAlso the slant delays sometimes appear to have more than one localmaximum and this ambiguity makes room for tpeaki to be fine-tunedconsidering the underlying assumptions

Next we select a pair of stations i j and determine the traveltime tij given by Eq (10)

tijinej tpeaki tpeakj i j 1 2 N (10)

The distance between the two stations projected onto the lineperpendicular to the wave frontdn is used to compute the speed ofthe wave front vn as shown in Eq (11)

vn dn

tijinej(11)

Note that this speed estimate contains the velocity componentresulting from the movement of the satellite in addition to that fromactual wave-front motion Thus the (notional) IPP velocity iscomputed and removed so that the resulting wave-front velocityestimates are referenced to a fixed point on the ground (such as anLGF site) IPP velocity and direction are computed from the CORS

data which give the notional IPP locations for each satellite at 30 sintervals The purpose of using IPP velocity even though the IPPconcept is not otherwise used in defining this threat model is tocapture in an average sense the speed of the ray-path motion throughthe ionosphere We repeat this computation for all combinations ofstations and obtain a range of front-speed estimates that are normal tothe ionospheric front Finally we take themean value of the resultingspeed estimates which is the manually estimated and validatedwave-front velocity with respect to the ground

VI Results CONUS Ionospheric AnomalyThreat Model

In this section we analyze two case studies following the processdescribed in Sec V These two cases are of particular interest becausethey give the maximum dual-frequency verified slopes we observedat high elevation (Sec VIA) and at low elevation (Sec VIB)respectively Both cases were observed from the OhioMichigancluster shown in the inset of Fig 3 and show very weak elevationdependence We then summarize the resulting threat model forCONUS

A Case Study 1 High Elevation

Figure 8 shows the slant ionospheric delay from this event asobserved by seven CORS reference stations in northern Ohio andsouthern Michigan as they tracked GPS SVN 38 Note the rapidgrowth in delay associatedwith the passage of the leading edge of theplume of enhanced delay shown in Fig 2 in just under an hourfollowed by an interval of erratic variation in ionospheric delaywithin the plumewhile the overall delay remains high followed by asudden steep drop-off corresponding to the duskward edge of theplume The JPL-processed dual-frequency delays (Fig 9a) andelevation angles (Fig 9c) over time are shown for ZOB1 (red) andGARF (green) which are separated by only 512 km The dual-frequency estimate of the sloperIDF between these stations is shownin Fig 9b

Data outages on both ZOB1rsquos and GARFrsquos dual-frequencymeasurements are visible in Fig 9a The drop from 30 to 18 m on theGARF (green) curve at 2055UTand on ZOB1 (red) at 2100 UT callsinto question the reliability of the dual-frequency estimate of theslope For this reason we validate the presence of an actual iono-spheric spatial anomaly within this data by comparing rIDF (blue)with the single-frequency estimate of the slope rICMC (red) inFig 10 The data outages present from 2055 to 2100 UT in rIDF donot exist in the L1-only measurements confirming that the data gapsare limited to L2measurements The general trend over time betweenthe two slope measurements agrees including the time during whichL2 data outages occur Based on the similarity of this event toobservations (Fig 8) by other receivers in the vicinity we can

Fig 8 Dual-frequency estimates of slant ionospheric delay from JPL-

processed CORS data vs UT hour for select stations in the Ohio

Michigan cluster tracking SVN 38 at high elevation

DATTA-BARUA ETAL 1147

confirm amaximum slope observed due to this event of 413 mm=kmat 2100 UT

B Case Study 2 Low Elevation

We also investigate data to show the largest-magnitude ion-ospheric gradients that we have validated at low elevation (less than15 deg) at 2130 UT Figure 11 is a plot of the dual-frequencymeasurements of slant ionospheric delay over time at these stationswhen observing SVN 26 to the northwest at about 12 deg elevation(not shown) The two stations FREO and WOOS (see inset of Fig 3for location) begin at 15 m and rise to over 45 m of slant delay as the

LOSs from these stations to SVN 26 pass through the filament ofenhanced ionospheric delay

We analyze the WOOSndashGARF pair of stations to estimate theionospheric spatial gradient between them The dual-frequency-based slope rIDF is plotted in blue on Fig 12 The slope rises from0 mm=km to nearly 400 mm=km as the delay for WOOS rises to45 m during passage through the anomalous region while GARFholds steady at about 15ndash18m There is a dual-frequency data gap forGARF at 2054 UT that contributes to the data gap inrIDF howeverthe outage is not simultaneous with the highest slope observedbetween this station pair

The dual-frequency estimate is compared with the single-frequency estimate of the ionospheric spatial gradientrICMC (greenon Fig 12) Continuous single-frequencymeasurements exist exceptat one epoch at 2052 UT at which time the L1 measurements had avisible cycle slip that was manually removed Based on theagreement between the two measurements we conclude that thehighest slope between the stationsWOOS and GARF occurs at 2120UT and is about 360 mm=km

C LAAS Ionospheric Threat Space

Using the method described in Sec V and illustrated in thesubsections above numerous other instances of high differentialdelay between receivers closer than 100 km were investigated frommultiple storm days in CONUS listed in Table 1 This was amultistage multiperson process spanning data from storms from

20 202 204 206 208 21 212 214 216 218 220

10

20

30

40Dual-frequencyZOB1 GARF SV38

Sla

nt

del

ay (

m)

ZOB1

GARF

20 202 204 206 208 21 212 214 216 218 22-200

0

200

400

600

Ion

o s

lop

e (m

mk

m)

20 202 204 206 208 21 212 214 216 218 2240

50

60

70

80

Ele

vati

on

(d

eg)

Time (hour of 11202003)

a)

b)

c)

Fig 9 Maximum ionospheric gradient observed at high elevation

a) dual-frequency estimates of slant ionospheric delay b) spatial rate of

change and c) elevation from JPL-processed CORS data vs UT hour for

receivers ZOB1 and GARF tracking SVN 38 at high elevation

20 202 204 206 208 21 212 214 216 218 22-50

0

50

100

150

200

250

300

350

400

450

Time (hour of 11202003)

Ion

o S

lop

e (m

mk

m)

ZOB1 and GARF Slopes Comparison SV38

DF Slope

L1 Slope

Fig 10 Spatial rate of change of ionospheric delay over time between

two stations in OhioMichigan cluster viewing SVN 38 at high elevation

from rIDF (blue) and rICMC (red)

207 208 209 21 211 212 213 2140

5

10

15

20

25

30

35

40

45

50Iono Delay for Selected Stations SV 26 Elevation100689ordm - 12078ordm

Time(Hour of 11202003)

Sla

nt Io

no D

elay

(m

)

FREO

WOOS

LSBN

GARFGUST

Fig 11 Dual-frequency estimates of slant ionospheric delay from JPL-processed CORS data vs UT hour for select stations in the Ohio

Michigan cluster tracking SVN 26 at low elevation

208 209 21 211 212 213 214-50

0

50

100

150

200

250

300

350

400

Time (hour of 11202003)

Ion

o S

lop

e (m

mk

m)

WOOS and GARF Slopes Comparison SV26

DF Slope

L1 Slope

Fig 12 Spatial rate of change of ionospheric delay over time between

two stations inOhioMichigan cluster viewing SVN26at lowelevation as

from rIDF (blue) and rICMC (green)

1148 DATTA-BARUA ETAL

2000ndash2005 during the maximum and waning phases of the mostrecent solar cycle

Figure 13 summarizes the results of this data analysis process bypopulating the threat space with cases observed in CORS data fromthe storms examined In Fig 13a the instances of anomalousgradients are plotted as a function of slope (mm=km) on thehorizontal axis and ground speed (m=s) on the vertical axis Thepoints that were identified in dual-frequency data and subsequentlyverified with single-frequency L1 code-minus-carrier data aremarked with diamonds The points that were estimated from therICMC directly are shown as triangles These same points are plottedin Fig 13b as a function of elevation angle (degrees) on the horizontalaxis and slope (mm=km) on the vertical axis

Note that width estimates do not appear in this figure Thewidth ofthe TEC rampwas generally difficult to assess from the available datagiven the typical 50ndash100 km separation of CORS reference stationsIn addition the integrity results (ie the threat model impacts onLAAS users) were not found to be sensitive to this parameter [27]

Empirical bounds are fitted to the points shown in Fig 13 in orderto limit the threat space to the maximum gradients discovered andvalidated These bounds are marked with solid lines in Fig 13Figure 13a shows that for events moving at speeds less than 90 mswith respect to the ground a maximum slope of 150 mmkm boundsthe events observed Figure 13b shows the upper bound on themaximum gradient of this threat model as a function of satelliteelevation angle for speeds greater than 90 ms a slope of375 mm=km at low elevation increasing linearly at a rate of 1 mmkm from 15 to 65 deg elevation to reach 425 mm=km at highelevation For ground speeds below 90 ms the maximum slope is150mmkm independent of elevation These bounds slightly exceedthe largest gradients validated from the data analysis due to marginadded to account for measurement error In addition to the plottedmaximum gradient and speed bounds width (distance between highand low delay regions) is delimited to be between 25 and 200 km andtotal slant differential delay up to 50 m [2236]

Figure 13a shows a maximum estimated ionospheric front speedof about 600 m=s with respect to the ground and the speed was

limited to be nomore than 750 m=s This bound is based on a numberof considerations First in practice the absolute speed is of secondaryimportance to the front speedwith respect to the aircraft andLGF ray-paths (or effectively their IPPs) The relative motion permitsdetectability in fact the estimates of speed were very sensitive to thisrelative motion There is a real (albeit unlikely) possibility that IPPspeed might cancel out the actual ionospheric front speed relative tothe ground and thus prevent time-based LGF monitoring fromdetecting very large gradients for several minutes This possibilitysignificantly increases the maximum possible ionosphere-inducederror for LAAS users beyond what was initially projected in workthat did not consider this masking possibility (eg [37]) It alsoaffects the ionospheric anomaly simulation algorithms described in[2338] as the method for selecting the worst possible ionosphericfront from the point of view of a given satellite is now dictated by thetheoretical speed of the resulting IPP

The threat space shown in Fig 13 is used to represent whatthe ionosphere is potentially capable of during rare extreme-ionospheric-storm conditions based on the validated events observedin the ionospheric data analysis described in Sec V However inadopting this threat model for the present wemust recognize that thevalidated events that have occurred to date are few in number sincethey are driven by the most extreme storms that have impacted theAmerican sector and cover a limited geographical area withinCONUS due to constraints imposed by the CORS receiver sites andequipage It is possible that localized events severe enough topotentially threaten LAAS users have occurred in CONUS but arenot included in the data used to construct the threat space Thereforewhile this threat space is deemed final for the purposes of CAT ILAAS certification [39] the anomaly mitigation algorithms haveadjustable parameters in case future ionospheric storms or dataanalyses reveal gradients that fall outside this threat space Anotherpossibility is that better understanding of how the ionospherebehaves during storm events will allow us to reduce the conservatismin the model as well as develop a more physically accuratedescription of how ionospheric anomalies propagate through LGFand user observations

Users can be protected from these situations by inflating theprotection bound that is broadcast to exclude geometries that wouldlead to intolerably large positioning errors [2338] Even thoughthere is no evidence of such large gradients on nominal days withoutthe ability to detect ionospheric storms in real time that is possessedby WAAS LAAS must assume that the ionosphere may be in adisturbed state at all times As a result geometries that would beunsafe during a storm are disallowed even though severe storms arevery unlikely to be present thus sacrificing precision approachavailability One method for excluding geometries that achieves anacceptable level of availability is described in [23]

VII Conclusions

This paper represents the culmination of several years ofteamwork in conducting as comprehensive a search as possible forpast extreme ionospheric events inCONUS thatwould impact LAASuser safety We have identified the ionospheric threat to users ofLAAS parameterized a model to represent it and applied a two-phase method of ionospheric spatial-gradient analysis and validationto quantify the magnitude of these threats This method consists ofautomatic processing of dual-frequency GPS carrier-phase measure-ments of ionospheric delay combined with manual comparison tosingle-frequency code-carrier divergence measurements to searchfor an upper bound on possible anomalous ionospheric gradientsThe final threat space of the model is ultimately driven by the largestof these points found but the parameters of this model have gonethrough a number of revisions in magnitude throughout this processThese values represent our best-validated observations during whatare recognized within the ionospheric community as the mostextreme geomagnetic storms during and following the most recentsolar maximum

The most time-consuming aspect of ionospheric anomaly dataanalysis is manual review of the apparent anomalies output by the

Fig 13 Ionospheric gradient threat space anomalous ionospheric

gradients observed plotted in threat model space as a function of a) slope

and ground speed and b) elevation and slope

DATTA-BARUA ETAL 1149

automated-search software In themajority of cases it was evident tohuman eyes that receiver or postprocessing errors created an apparentionospheric gradient that was not real and these observations wereset aside However a significant number of events that could not bediscounted remained andwere subjected tomore detailed analysis byteam consensus

As a result of this work we have validated low-elevation spatialgradients in the ionospheric delay at L1 as high as 360 and413 mm=km at high elevation These observations were made withCORS network stations in northern Ohio during the 20 Nov 2003ionospheric storm Station redundancy rules out the possibility ofthese gradients being generated by faulty receivers or significanterrors in receiver bias estimation Simultaneous observations onother satellites at similar elevations were also unusually highTherefore we conclude that the largest gradients observed herewerenot due to nonionospheric causes

The impact of these gradients on WAAS precision approachintegrity is negligible due to the implementation of the WAASextreme storm detector (however WAAS precision approachavailability is dramatically affected when severe storms occur)Mitigating the safety threats posed by ionospheric storms to users ofLAAS is achieved by disallowing specific user satellite geometriesduring precision approach at the cost of somewhat reducing LAASCAT 1 availability

Acknowledgments

The authors thank the FAA LAAS Program Office for theirsupport in the completion of this effort We would also like to thankspecific individuals who helped us in this work including ToddWalter Juan Blanch and Shankar Ramakrishnan at Stanford JohnWarburton and Tom Dehel of the Federal Aviation AdministrationWilliam J Hughes Technical Center and Attila Komjathy of theNASA Jet Propulsion Laboratory

References

[1] Enge P ldquoLocal AreaAugmentation ofGPS for the PrecisionApproachof Aircraftrdquo Proceedings of the IEEE Vol 87 No 1 Jan 1999pp 111ndash132doi1011095736345

[2] Enge P Walter T Pullen S Kee C Chao Y C and Tsai Y JldquoWide Area Augmentation of the Global Positioning SystemrdquoProceedings of the IEEE Vol 84 No 8 Aug 1996 pp 1063ndash1088doi1011095533954

[3] Misra P and Enge P Global Positioning System Signals

Measurement and Performance 2nd ed GangandashJamuna LincolnMA 2006

[4] Tascione T F Introduction to the Space Environment KriegerMalabar FL 1994

[5] Klobuchar J ldquoIonospheric Effects on GPSrdquo Global Positioning

System Theory and Applications edited by B W Parkinson and J JSpilker Vol 1 Progress in Astronautics and Aeronautics AIAAWashington DC 1996 pp 485ndash515

[6] Datta-Barua S Mannucci A J Walter T and Enge P ldquoAltitudinalVariation of Midlatitude Localized TEC Enhancement from Ground-and Space-BasedMeasurementsrdquo Space Weather Vol 6 No S10D062008doi1010292008SW000396

[7] Roble R G and Ridley E C ldquoThermospherendashIonospherendashMesosphere Electrodynamics General Circulation Model (TIME-GCM) Equinox Solar Cycle Minimum Simulations (30ndash500 km)rdquoGeophysical Research Letters Vol 21 No 6 1994 pp 417ndash420doi10102993GL03391

[8] Crowley G et al ldquoGlobal ThermospherendashIonosphere Response toOnset of 20 November 2003Magnetic Stormrdquo Journal of GeophysicalResearch Vol 111 No A10S18 2006doi1010292005JA011518

[9] Huba J D Joyce G and Fedder J A ldquoSami2 is Another Model ofthe Ionosphere (SAMI2) A New Low-Latitude Ionosphere ModelrdquoJournal of Geophysical Research Vol 105 No A10 2000 pp 23035ndash23053doi1010292000JA000035

[10] Bilitza D ldquoInternational Reference Ionosphere 2000rdquo Radio ScienceVol 36 No 2 MarchndashApril 2001 pp 261ndash275

doi1010292000RS002432[11] PiXWangC Hajj GA RosenGWilson BD andBaileyG J

ldquoEstimation of ExB Drift Using a Global Assimilative IonosphericModel An Observation System Simulation Experimentrdquo Journal of

Geophysical Research Vol 108(A2) No 1075 2003doi1010292001JA009235

[12] Schunk R et al ldquoGlobal Assimilation of Ionospheric Measurements(GAIM)rdquo Radio Science Vol 39 No RS1S02 2004doi1010292002RS002794

[13] Bust G S et al ldquoFour-Dimensional GPS Imaging of Space WeatherStormsrdquo Space Weather Vol 5 No S02003 2007doi1010292006SW000237

[14] Klobuchar J A Doherty P H and El-Arini M B ldquoPotentialIonospheric Limitations to Wide-Area Differential GPSrdquo Proceedingsof the 16th International Technical Meeting of the Satellite Division of

The Institute Of Navigation (IONGPS 1993) Salt Lake City UT 1993pp 1245ndash1254

[15] Klobuchar J A and Kunches J ldquoEye on the Ionosphere The SpatialVariability of Ionospheric Range Delayrdquo GPS Solutions Vol 3 No 32000 pp 70ndash74doi101007PL00012808

[16] Walter T et al ldquoRobust Detection of Ionospheric IrregularitiesrdquoProceedings of IONGPS 2000 httpwaasstanfordedu~wwupapersgpsPDFtoddion00pdf Salt Lake City UT 2000 pp 209ndash218

[17] Christie J R I Ko P-Y Hansen A Dai D Pullen S Pervan Band Parkinson B ldquoThe Effects of Local Ionospheric Decorrelation onLAAS Theory and Experimental Resultsrdquo Proceedings of the Instituteof Navigation National Technical Meeting 1999 pp 769ndash777

[18] ldquoMinimum Operational Performance Standards for GPS Local AreaAugmentation System Airborne Equipmentrdquo Radio TechnicalCommission for Aeronautics Washington DC Publ DO-253C2008

[19] Lee J Pullen S Datta-Barua S and Enge P ldquoAssessment ofNominal Ionosphere Spatial Decorrelation for GPS-Based AircraftLanding Systemsrdquo Journal of Aircraft Vol 44 No 5 2007 pp 1662ndash1669doi102514128199

[20] Datta-Barua SWalter T Pullen S LuoM Blanch J and Enge PldquoUsing WAAS Ionospheric Data to Estimate LAAS Short BaselineGradientsrdquo Proceedings of the Institute of Navigation National

Technical Meeting 2002 pp 523ndash530[21] Luo M Pullen S Akos D Xie G Datta-Barua S Walter T and

Enge P ldquoAssessment of Ionospheric Impact on LAAS Using WAASSupertruth Datardquo Proceedings of the ION 58th Annual Meeting and

CIGTF 21st Guidance Test Symposium 2002 pp 175ndash186[22] Ene A Qiu D Luo M Pullen S and Enge P ldquoAComprehensive

Ionosphere Storm Data Analysis Method to Support LAAS ThreatModel Developmentrdquo Proceedings of the Institute of Navigation

National Technical Meeting 2005 pp 110ndash130[23] Ramakrishnan S Lee J Pullen S andEnge P ldquoTargetedEphemeris

Decorrelation Parameter Inflation for Improved LAAS AvailabilityDuring Severe Ionosphere Anomaliesrdquo Proceedings of the 2008

National TechnicalMeeting of the Institute of Navigation pp 354ndash366[24] Komjathy A L Sparks and A J Mannucci ldquoGenerating High

Precision Ionospheric Ground-Truth Measurementsrdquo US PatentNo 7289061 B2 filed 30 Oct 2007

[25] Datta-Barua S ldquoIonospheric Threats to the Integrity of Airborne GPSUsersrdquo PhD Dissertation Stanford Univ Palo Alto CA 2008

[26] Luo M Pullen S Dennis J Konno H Xie G Walter T Enge PDatta-Barua S and Dehel T ldquoLAAS Ionosphere Spatial GradientThreat Model and Impact of LGF and Airborne MonitoringrdquoProceedings of the 16th International TechnicalMeeting of the Satellite

Division of the Institute of Navigation IONGPSGNSS 2003 pp 2255ndash2274

[27] Luo M Pullen S Walter T and Enge P ldquoIonosphere SpatialGradient Threat for LAAS Mitigation and Tolerable Threat SpacerdquoProceedings of the 2004 National Technical Meeting of the Institute of

Navigation pp 490ndash501[28] National Geodetic Survey CORS Team ldquoCORS Continuously

Operating Reference Stationsrdquo httpwwwngsnoaagovCORS[retrieved 6 Jan 2009]

[29] National Geophysical Data Center ldquoSpace Physics Interactive DataResourcerdquo 2005 httpspidrngdcnoaagovspidr [retrieved1 March 2005]

[30] Sugiura M and Kamei T ldquoEquatorial Dst Index 1957ndash1986rdquoInternational Association of Geomagnetism and Aeronomy Vol 401991 pp 7ndash14

[31] Menvielle M and Berthelier A ldquoThe K-Derived Planetary IndicesDescription and Availabilityrdquo Reviews of Geophysics Vol 29 No 3

1150 DATTA-BARUA ETAL

removed separately [24] WAAS receiver redundancy allows forvoting to remove possible artifacts due to problems on individualreceivers These data are reliable but geographically sparse relative tothe LAAS service area as the closest WAAS reference stations are255 km apart These locations are denoted with diamonds in Fig 3

To estimate the spatial rate of change possible in a LAASconfiguration the difference in delay along two lines of sight (LOSs)with a distance between them that is characteristic of LAAS must bemeasured One technique that is especially useful for analyzingdifferential delayswhen receivers are sparsely distributed is the time-stepmethod in which the IPP traversal distance over time substitutesfor the LAAS ground stationaircraft separation [19]

WAAS data was used to identify times and regions exhibiting highrates of change as computed by the time-step method during theionospheric storm of 6 April 2000 [20] During this storm a largeSED passed over CONUS and its duskward edge caused highdifferential delays Figure 4 shows the slant ionospheric delay inmeters at each of the threeWAAS receivers at ZDCas they track SVN40 The horizontal axis below each subplot marks the elapsed time inseconds For each receiver I (red) I (blue) and ICMC (green) areplotted Both I and ICMC are recentered to the time-averaged codemeasurement of the delay I to account for the ambiguous integernumber of cycles

Over a time interval of 110 s demarcated with vertical lines onFig 4 the receiver tracked through a drop in the slant ionosphericdelay of about 8 m There were no cycle slips (discontinuities in themeasured ionospheric delay due to temporary receiver trackingproblems) for any of the receivers during the time interval inquestion The identical time evolution shown by all three receivers onall three forms of estimated delay indicates that the observedanomaly was not due to receiver bias

A similar event for an independent LOS an hour and a half earlieron the same day further confirms the ZDC-SVN 40 anomaly Thereceiver measurements of the ionosphere between theWAAS stationZBWand SVN 24 show that while station ZBW was tracking SVN24 during a span of 150 s the slant ionospheric delay on this LOSdropped by about 10 m Given SVN 24rsquos position in the sky duringthis time interval the IPP of the ray-path traveled about 15 km Thefact that these observations represent two independent LOSsindicates that neither biases at an individual receiver or satellite norcycle slips contributed to the high apparent decorrelation Thereforethe anomalous events that affected LOS ZDC-SVN 40 and LOSZBW-SVN 24 were both records of actual ionospheric events

However the gradients they imply (8m at L1 over 7 km separationand 10 m over 15 km) may be artificially inflated for the followingreason The anomaly at station ZBWas it tracked SVN 24 precededthe anomaly along LOS ZDC-SVN 40 by 15 h Ionospheric TECmaps for this storm period shown in the Appendix to [25] roughlyindicate that the storm front whose boundary runs roughly east-westrecedes southward for the duration Having first been traversed by anIPP from station ZBW it progresses south to be traversed by an IPPfrom ZDC 15 h later Based on this observation to first order its

velocity vfront 110 m=s southward The IPP associated with ZDC-SVN 40 was moving primarily northward with ground speedvIPP 7 km=110 s 63 m=s respectively The velocity of thestorm front is nonnegligible compared with the IPP velocity Sincethe velocities were directed roughly opposite to each other theadditive effect of the relative velocities would make the spatialgradient in the ionosphere appear to be a factor of two ormoreworsethan it actually was Instead of a 7 kmwidth thewidthw of the stormfront was more likely

w vfront vIPP t 110 63 110 19 030 m (5)

This means that the purely spatial gradient of the ionosphere wasmore likely 8 m of slant delay difference over a 19 km range w asshown in Eq (5) giving a 420 mm=km spatial rate of change of slantdelay

From this event we learned about the potential existence of spatialrates of change in ionospheric delay with magnitudes that could notbe bounded by the LAAS nominal broadcast correction We alsoobserved that estimating the spatial rate of change is complicated bythe mixing of spatial and temporal variations in the ionospherethrough SED motion as well as the motion of LOSs relative to theionospheric structure

III Ionospheric Threat Model Parameterization

The discovery of high spatial gradients at the edges of ionosphericSEDs combined with relative motions of the ionosphere and LOSswas used to develop an ionospheric threat model for identifyingpossible threats to LAAS users In this section we define a model foran ionospheric wave front and associated model parameters

Based on the findings in Sec II the ionospheric threat is modeledas a spatially linear semi-infinite wave front with constantpropagation speed Figure 5 illustrates this model The model isparameterized by the slope g of the ramp and its widthw the productof which yields a maximum ionospheric delay D and the groundspeed v of wave front The observations in Fig 4 of a constant highdelay a period of decline at a fairly constant rate and a period ofconstant low delay supports the piecewise linear model A higherorder model may fit the actual data better However simplicity in themathematical form of an ionospheric threat model enables easierassessment of impact on users through LAAS simulation tools andidentification of mitigation strategies

Themaximum ionospheric delay difference is upper-bounded by amaximum value which was set based on observations Slope andwidth values whose product creates a maximum delay differenceabove the bound on D are not part of the threat model and arediscarded This limit is conservative because it allows for possibleionospheric configurations that may be physically impossible but itkeeps the simulation search space tractable In any case as Luo et al[26] showed the extreme regions of the search space will be easilydetected by the LGF within the time to alarm when the ionosphericfront is verywide and thus sweeps across thefield of viewof the LGFFortunately the impact of ionospheric wave fronts on LAAS is notvery sensitive to width thus the choice was made to include a rangeof possible frontwidths from25 to 200 km in the threatmodel Largerwidths would not produce gradients sufficiently sharp to bethreatening and smaller widths would not cause much difference inLAAS impact simulation results [27] Thus data analysis proceduresare devoted to estimating the slopeg and speed v of ionosphericwavefront (Sec V)

Fig 4 Slant ionospheric delay in m between WAAS receivers at

Washington DC and GPS satellite 40 on 6 April 2000

Max Iono delay

D = w g

Front Speed (v )

Nominal IonoWidth (w)

Iono Front

Slope (g)

Fig 5 Ionospheric wave-front model

1144 DATTA-BARUA ETAL

As noted above the gradient is assumed to represent a linearchange in slant ionospheric delay between maximum and minimumdelays As a result the bounding slant gradients are also (at leastpotentially) functions of satellite elevation

To recap this threat model assumes severe ionospheric spatialgradients take the form of straight horizontally semi-infinite wavefronts of linearly varying TEC that move at constant speed anddirectionwith respect to the ground In addition the slope of the rampitself is not time-varying These are simplifications of reality but arereasonable for the duration of an aircraft approach These assump-tions are adequate because deviations from them do not materiallychange the final result except if most-evil variations (compoundingthe worst-case upon the worst-case) are allowed

The ionospheric threat model does not include other parametersthat are necessary for simulating and assessing impact on LAAS-equipped aircraft such as the orientation of the ionospheric wavefront with respect to the aircraft and runway the horizontal approachspeed of the aircraft the approach direction of the ionospheric frontthe initial position of the ionospheric front with respect to the aircraftat the start of the simulation and the velocities of the IPPs which aredriven by satellite and receiver motion Figure 1 illustrates anexample of one possible configuration of the ionospheric threatmodeluserrunwaysatellite scenario

IV Data

The remaining sections of this paper are concerned withidentifying theworst observed values of the ionospheric threatmodelparameters of slope and speed at different ranges of elevation anglesExisting ionospheric models do not capture anomalous behaviorsuch as that observed on 6 April 2000 so our threat-space devel-opment for the ionosphere is data-driven Precise estimates ofionospheric delays can be obtained using dual-frequency GPSmeasurements from networks of stations and sophisticated post-processing algorithms

The US National Geodetic Survey makes dual-frequency GPSdata publicly available in RINEX format from a network ofcontinuously operating reference stations (CORS) [28] CORS hashundreds of stations in CONUS (sites as of 29 Oct 2003 are shownin Fig 3) and permits ionospheric measurement comparisons acrossbaselines of tens of kilometers Before analysis geographicalclusters of the highest-density receiver regions in the United Statesare targeted to provide the best possible spatial resolution Eightclusters overCONUSof around 21 square degrees of average area aredefined and boxed in Fig 3 Each of these clusters contains 10ndash30receivers with baseline separations of 15ndash200 km Most of the dataanalysis is performed for stations inside these clusters In some casesadditional stations adjacent to these clusters are also used to bettercapture ionospheric wave-front behavior that extended beyondcluster boundaries

The dates fromwhich data were collected and analyzed are shownin Table 1 along with the publicly available indices of globalgeomagnetic activity planetary K (Kp) and disturbance storm time(Dst) and geomagnetic storm class (G-class) and WAAS coverageParticular geographic regions of focus if applicable are shown

The values of Kp Dst G-class and WAAS coverage are used tohelp target days onwhich anomalous behavior in CONUSwas likelyto have occurred Because the ionosphere is coupled to themagnetosphere which is in turn driven by the solar wind measuresof geomagnetic activity provide information about the ionosphere byproxy The Dst index measures activity in the worldwide geomag-netic field [2930] with negative deviations indicating ionosphericcurrents and activity Kp is a three-hour composite index based ongeomagnetic activity measured at several midlatitude stationsprimarily located in the northern hemisphere [3132] Kp ranges inthirds of an index unit from 0 (no activity) to 9 (extreme activity) TheSpace Weather Prediction Center of the National Oceanic andAtmospheric Administration has developed a geomagnetic stormscale and associated descriptors for use by both the technicalcommunity and the general public [33] The scale varies from G1throughG5 and the storm classes associated with each level in orderof increasing intensity are minor moderate strong severe andextreme

The percentage of WAAS coverage indicates the degree to whichthese storms introduced nonlinear spatial variations over CONUS(viewed over separations of hundreds of km between WAASreference stations) limiting WAAS availability for precision ap-proach While the other measures shown in Table 1 are globalWAAS coverage is a daily summary value that indicates how muchimpact each storm had in the American sector where the CORSstations are sited For the dates occurring before WAAS initialoperating capability (IOC) official coverage percentages do not exist

The data collected from the CORS network have been assimilatedand had biases removed by the use of the global ionosphericmapping(GIM) software atNASArsquos Jet PropulsionLaboratory (JPL)With theadditional use of the publicly available International GNSS Servicestation data worldwide GIM estimates and removes satellite andreceiver interfrequency biases to provide high-precision ionosphericmeasurements The dual-frequency ionospheric observables IDF arecomputed by leveling the phase estimates I using the codeestimates I The processing with a leveling function f is describedin detail in [34] The resulting data are provided at 30 s intervals

IDF fI I (6)

CORS ionospheric estimates are processed in a similar manner tothose ofWAAS (see Sec II) but the lack ofmultiple receivers at eachsite means that anomalies due to receiver glitches cannot be votedout In addition the quality of CORS receiver equipage and siting ismuch more variable than that of WAAS reference stations thustypical CORS receiver measurement errors are several times higherthan that of WAAS receivers The screening algorithms used at JPLare based on an error detection threshold that was set to allow moredata points and minimize the risk of discarding true ionosphericevents

V Data Analysis Procedure

The objective of this section is to produce the LAAS threat spaceon parameters g and v using the data from closely-spaced CORSstations described above The procedure for processing CORS

Table 1 Ionospheric storm dates and conditions investigated

Day (UT mmddyy) Kp Dst Geomagneticstorm class

WAAS coverage Focus region

040600 83 287 Severe None (pre-IOC) NE corridor040700 87 288 Extreme None (pre-IOC) NE corridor071500 90 289 Extreme None (pre-IOC) mdashmdash

071600 77 301 Strong None (pre-IOC) mdashmdash

090702 73 163 Strong None (pre-IOC) mdashmdash

102903 90 345 Extreme 0 mdashmdash

103003 90 401 Extreme 0 TXndashOKndashLAndashAR103103 83 320 Severe 0 FLndashGA112003 87 472 Extreme 0 OHndashMI071704 60 80 Moderate 688 TXndashOKndashLAndashAR

DATTA-BARUA ETAL 1145

receiver data identifying anomalously high spatial rates of changevalidating anomalies that are due to ionospheric events andestimating parameters in terms of the threatmodel of Sec III is shownin Fig 6 The starting point is raw L1 and L2 measurements fromselected clusters of CORS stations An automated algorithm flowingdown the left-hand side of the figure involves using the JPL-processed CORS-based estimates of ionospheric delay to computeapparent spatial gradients and front speeds and screen them forobvious receiver or data-processing errors The remaining points arecompared manually (right-hand side of Fig 6) to estimates derivedfrom raw CORS data The following sections trace through themethodology outlined in the flowchart in Fig 6

A Ionospheric Spatial-Gradient Estimation

1 Station Pair Method

Our approach involves considering selected pairs of CORSstations in the JPL-processed database as though they represent anLGF-user receiver pair (with a static user position) This techniquecalled the station pair method is illustrated in Fig 7 For each epocht the delays at each of two stations i and j viewing the same satellite kare differenced The slope of ionospheric delayrI between all pairsof receivers looking at each satellite is estimated by dividing thedifferential slant delay by the baseline distance between the tworeceivers

rIt jIki t Ikj tjkxi xjk

(7)

The station pair method unlike the time-step method (Sec II)does not artificially inflate ionospheric gradients due to relativemotions of IPPs and the ionosphere [19]

The dual-frequency data in each CORS cluster are separated intofive elevation bins 0ndash12 12ndash20 20ndash30 30ndash45 and 45ndash90 deg Foreach LOS elevation range receivers in each cluster are grouped intopairs and IDF is used for I in Eq (7) to give a dual-frequencyestimate of the gradient rIDF

2 Automated Screening

AfterrIDF is computed an automated screening process (bottom-left corner of Fig 6) isolates the most credible events above a chosenthreshold (100 mm=km) and eliminates those gradients that areapparently due to nonionospheric causes Cases for which tworeceivers are effectively collocated (less than 100m apart) or the slantgradient rIDF does not vary in time are attributed to an interreceiverbias and eliminated Cases for which ionospheric delay measure-ments from one receiver do not vary in time are attributed to a faultyreceiver and eliminated

Variations greater than 15 mm=s ionospheric delay rate of changeare flagged for possible ionospheric activity The 15 mm=s thresholdis set to be an order of magnitude lower than the largest time rate ofchange observed for the 29 Oct 2003 storm day [35] in order toallow significant rapid changes in the delay value to pass screening aspossible ionospheric events The advantage of this threshold is thatreal ionospheric events are less likely to be eliminated as falsenegatives The tradeoff is that false alarms occur when the datacontain cycle slips causing the delay value to jump suddenlybetween two successive measurement points These cases have to beeliminated manually from the screening results

3 Manual Validation

The plausible ionospheric events that survive the computer-automated screening process are analyzed manually For each ofthese events visual analysis determineswhether themagnitude of thegradients together with the length of time that these gradientsmanifest themselves look reasonable or aremore likely to be artifactsof faulty receiver tracking

The first step in verifying an event is by comparison againstsimultaneous observations of slant delay from other nearby receiversin the same cluster This can be thought of as cluster-wide voting thatoperates in a similar manner to the voting among colocated receiversat each WAAS reference station If other nearby CORS receiversexhibit similar ionospheric delay patterns to the pair that shows themost-extreme spatial decorrelation the event is very probably realand is not limited to a single receiver fault On the other hand if theobservations were influenced by receiver artifacts in the data it isunlikely that amajority of the receivers in the cluster contain the sameor similar artifacts

To support this comparison plots are constructed that extendacross the entire period of time for which data was extracted so thatthe receiver behavior can be analyzed both in the presence ofdisturbed ionosphere and also during calm ionospheric conditions oneither side of the disturbance While the electron content within theionosphere is expected to vary wildly during the actual storm delaymeasurements from the different receivers within a CORS cluster areexpected to converge to similar values at times of nominalionospheric activity Quiet periods of ionospheric behavior help toestablish a baseline for the affected receiver cluster and give anindication of any possible interreceiver biases

The next step in validating extreme gradients (top-right corner ofFig 6) is to estimate approximate wave-front parameters from rawL1 code-minus-carrier measurements ICMC and compare them to theobservations from the JPL-processed dual-frequency data IDF TheL1-only estimates of these slopesrICMC given by using ICMC for theslant delay I in Eq (7) provide a picture of gradients that is notsubject to fragile L2 tracking loops The L1-only estimates are notexact however because the integer ambiguity N1 has not beeneliminated

Two methods are used to remove the integer ambiguity for high-elevation and low-elevation LOSs respectively For high elevationthe postprocessed L1L2 estimate of absolute differential sloperIDFat a timewell before or after thewave front passes is used to initializethe single-frequency ionospheric gradient estimates rICMC A pointin time is picked at which the measurements rICMC are aligned torIDF by subtracting a constant bias from all the measurement of oneof the receivers equalizing the value that they measure at the choseninstant Since the differential delay between a pair of receivers isobserved this is equivalent to choosing a reference zero-slope point

L1 code and carrier

L1 code and carrier L2 carrier

ldquoRawrdquo IGSCORS Data

JPL Ionosphere ldquoTruthrdquo Processing

Ionosphere Delay Estimates

Find Maximum Apparent Gradients

(Station Pair Method)

Screening Process (Automated)

Database of Extreme Gradients Erroneous Receiver

Steps and L1L2 Biases Removed Investigate

Remaining Points

Remove ldquoQuestionablerdquoObservations

Estimate Gradients from L1 code-minus-carrier

Output Database of Maximum Gradients

Compare L1L2 and L1 CMC Gradient Obs

ldquoValidatedrdquoMax Gradient

Database

Fig 6 Ionospheric anomaly data analysis procedure

Fig 7 Station pair method Not to scale

1146 DATTA-BARUA ETAL

in rICMC at a particular instant in time and then expressing all otherslope measurements relative to it Thus L1 CMC ionosphericgradient estimates form lower bounds on the true gradient but theseestimates are accurate enough that they can be used to validate theL1L2 observations

For low-elevation satellites the track may not be long enough tosample through quiet ionosphere In this case the single-frequencygradient estimate is leveled to the mean of the JPL-processed dual-frequency estimate rIDF as shown in Eq (8)

r ~ICMCt rICMCt rICMC rIDF (8)

Anomalous ionospheric events not eliminated by this final manual

comparison to single-frequency estimates r ~ICMC of the spatialvariation are deemed to be validated measurements rIDFValidated andare used to generate bounds on the threat model parameter g

gmaxtrIDFValidated (9)

B Ionospheric Front Speed Estimation

Observations of severe gradients that are validated as described inSec VA are studied inmore detail to characterize them in the contextof the ionospheric threatmodel geometry The automated code used athree-station-based trigonometric algorithm to estimate the speedand direction of wave-front propagation with respect to the ground(see [22] for details) The travel time of the frontwhich swept a pair ofstations is measured to compute the speed and the third station isused to observe the front direction This algorithm proved to be verysensitive to measurement errors and limitations of the threat model(in particular the assumption that the wave front is a straight line)when extrapolated to cover three (instead of two) stations

To overcome the weakness of the automated ionospheric frontspeed computation method a manual two-station-based method isused that takes wave-front orientation (estimated manually based onmany stations) as an input so that only speed needs to be solved forFirst we form groups that typically include four to eight nearbyCORS receiverswhich show similar ionospheric delay patterns to thesatellite-receiver pair from which anomalous gradients wereobserved and verified By observing the time history of ionosphericslant delays from the stations observing the same satellite the peakdelay times tpeaki at which the slant delay of the station i reached thelocal maximum value are determined Because ionospheric delays ofthese nearby stations exhibit similar patterns the order in which thestations were affected by the ionospheric front can be deduced fromtpeaki This order and the locations of the affected stations make itpossible to approximately determine the forward propagationdirection of the front Since it was assumed that the wave front is astraight line and moves with constant speed which is not true inreality the sequence and tpeaki must be carefully adjusted manuallyAlso the slant delays sometimes appear to have more than one localmaximum and this ambiguity makes room for tpeaki to be fine-tunedconsidering the underlying assumptions

Next we select a pair of stations i j and determine the traveltime tij given by Eq (10)

tijinej tpeaki tpeakj i j 1 2 N (10)

The distance between the two stations projected onto the lineperpendicular to the wave frontdn is used to compute the speed ofthe wave front vn as shown in Eq (11)

vn dn

tijinej(11)

Note that this speed estimate contains the velocity componentresulting from the movement of the satellite in addition to that fromactual wave-front motion Thus the (notional) IPP velocity iscomputed and removed so that the resulting wave-front velocityestimates are referenced to a fixed point on the ground (such as anLGF site) IPP velocity and direction are computed from the CORS

data which give the notional IPP locations for each satellite at 30 sintervals The purpose of using IPP velocity even though the IPPconcept is not otherwise used in defining this threat model is tocapture in an average sense the speed of the ray-path motion throughthe ionosphere We repeat this computation for all combinations ofstations and obtain a range of front-speed estimates that are normal tothe ionospheric front Finally we take themean value of the resultingspeed estimates which is the manually estimated and validatedwave-front velocity with respect to the ground

VI Results CONUS Ionospheric AnomalyThreat Model

In this section we analyze two case studies following the processdescribed in Sec V These two cases are of particular interest becausethey give the maximum dual-frequency verified slopes we observedat high elevation (Sec VIA) and at low elevation (Sec VIB)respectively Both cases were observed from the OhioMichigancluster shown in the inset of Fig 3 and show very weak elevationdependence We then summarize the resulting threat model forCONUS

A Case Study 1 High Elevation

Figure 8 shows the slant ionospheric delay from this event asobserved by seven CORS reference stations in northern Ohio andsouthern Michigan as they tracked GPS SVN 38 Note the rapidgrowth in delay associatedwith the passage of the leading edge of theplume of enhanced delay shown in Fig 2 in just under an hourfollowed by an interval of erratic variation in ionospheric delaywithin the plumewhile the overall delay remains high followed by asudden steep drop-off corresponding to the duskward edge of theplume The JPL-processed dual-frequency delays (Fig 9a) andelevation angles (Fig 9c) over time are shown for ZOB1 (red) andGARF (green) which are separated by only 512 km The dual-frequency estimate of the sloperIDF between these stations is shownin Fig 9b

Data outages on both ZOB1rsquos and GARFrsquos dual-frequencymeasurements are visible in Fig 9a The drop from 30 to 18 m on theGARF (green) curve at 2055UTand on ZOB1 (red) at 2100 UT callsinto question the reliability of the dual-frequency estimate of theslope For this reason we validate the presence of an actual iono-spheric spatial anomaly within this data by comparing rIDF (blue)with the single-frequency estimate of the slope rICMC (red) inFig 10 The data outages present from 2055 to 2100 UT in rIDF donot exist in the L1-only measurements confirming that the data gapsare limited to L2measurements The general trend over time betweenthe two slope measurements agrees including the time during whichL2 data outages occur Based on the similarity of this event toobservations (Fig 8) by other receivers in the vicinity we can

Fig 8 Dual-frequency estimates of slant ionospheric delay from JPL-

processed CORS data vs UT hour for select stations in the Ohio

Michigan cluster tracking SVN 38 at high elevation

DATTA-BARUA ETAL 1147

confirm amaximum slope observed due to this event of 413 mm=kmat 2100 UT

B Case Study 2 Low Elevation

We also investigate data to show the largest-magnitude ion-ospheric gradients that we have validated at low elevation (less than15 deg) at 2130 UT Figure 11 is a plot of the dual-frequencymeasurements of slant ionospheric delay over time at these stationswhen observing SVN 26 to the northwest at about 12 deg elevation(not shown) The two stations FREO and WOOS (see inset of Fig 3for location) begin at 15 m and rise to over 45 m of slant delay as the

LOSs from these stations to SVN 26 pass through the filament ofenhanced ionospheric delay

We analyze the WOOSndashGARF pair of stations to estimate theionospheric spatial gradient between them The dual-frequency-based slope rIDF is plotted in blue on Fig 12 The slope rises from0 mm=km to nearly 400 mm=km as the delay for WOOS rises to45 m during passage through the anomalous region while GARFholds steady at about 15ndash18m There is a dual-frequency data gap forGARF at 2054 UT that contributes to the data gap inrIDF howeverthe outage is not simultaneous with the highest slope observedbetween this station pair

The dual-frequency estimate is compared with the single-frequency estimate of the ionospheric spatial gradientrICMC (greenon Fig 12) Continuous single-frequencymeasurements exist exceptat one epoch at 2052 UT at which time the L1 measurements had avisible cycle slip that was manually removed Based on theagreement between the two measurements we conclude that thehighest slope between the stationsWOOS and GARF occurs at 2120UT and is about 360 mm=km

C LAAS Ionospheric Threat Space

Using the method described in Sec V and illustrated in thesubsections above numerous other instances of high differentialdelay between receivers closer than 100 km were investigated frommultiple storm days in CONUS listed in Table 1 This was amultistage multiperson process spanning data from storms from

20 202 204 206 208 21 212 214 216 218 220

10

20

30

40Dual-frequencyZOB1 GARF SV38

Sla

nt

del

ay (

m)

ZOB1

GARF

20 202 204 206 208 21 212 214 216 218 22-200

0

200

400

600

Ion

o s

lop

e (m

mk

m)

20 202 204 206 208 21 212 214 216 218 2240

50

60

70

80

Ele

vati

on

(d

eg)

Time (hour of 11202003)

a)

b)

c)

Fig 9 Maximum ionospheric gradient observed at high elevation

a) dual-frequency estimates of slant ionospheric delay b) spatial rate of

change and c) elevation from JPL-processed CORS data vs UT hour for

receivers ZOB1 and GARF tracking SVN 38 at high elevation

20 202 204 206 208 21 212 214 216 218 22-50

0

50

100

150

200

250

300

350

400

450

Time (hour of 11202003)

Ion

o S

lop

e (m

mk

m)

ZOB1 and GARF Slopes Comparison SV38

DF Slope

L1 Slope

Fig 10 Spatial rate of change of ionospheric delay over time between

two stations in OhioMichigan cluster viewing SVN 38 at high elevation

from rIDF (blue) and rICMC (red)

207 208 209 21 211 212 213 2140

5

10

15

20

25

30

35

40

45

50Iono Delay for Selected Stations SV 26 Elevation100689ordm - 12078ordm

Time(Hour of 11202003)

Sla

nt Io

no D

elay

(m

)

FREO

WOOS

LSBN

GARFGUST

Fig 11 Dual-frequency estimates of slant ionospheric delay from JPL-processed CORS data vs UT hour for select stations in the Ohio

Michigan cluster tracking SVN 26 at low elevation

208 209 21 211 212 213 214-50

0

50

100

150

200

250

300

350

400

Time (hour of 11202003)

Ion

o S

lop

e (m

mk

m)

WOOS and GARF Slopes Comparison SV26

DF Slope

L1 Slope

Fig 12 Spatial rate of change of ionospheric delay over time between

two stations inOhioMichigan cluster viewing SVN26at lowelevation as

from rIDF (blue) and rICMC (green)

1148 DATTA-BARUA ETAL

2000ndash2005 during the maximum and waning phases of the mostrecent solar cycle

Figure 13 summarizes the results of this data analysis process bypopulating the threat space with cases observed in CORS data fromthe storms examined In Fig 13a the instances of anomalousgradients are plotted as a function of slope (mm=km) on thehorizontal axis and ground speed (m=s) on the vertical axis Thepoints that were identified in dual-frequency data and subsequentlyverified with single-frequency L1 code-minus-carrier data aremarked with diamonds The points that were estimated from therICMC directly are shown as triangles These same points are plottedin Fig 13b as a function of elevation angle (degrees) on the horizontalaxis and slope (mm=km) on the vertical axis

Note that width estimates do not appear in this figure Thewidth ofthe TEC rampwas generally difficult to assess from the available datagiven the typical 50ndash100 km separation of CORS reference stationsIn addition the integrity results (ie the threat model impacts onLAAS users) were not found to be sensitive to this parameter [27]

Empirical bounds are fitted to the points shown in Fig 13 in orderto limit the threat space to the maximum gradients discovered andvalidated These bounds are marked with solid lines in Fig 13Figure 13a shows that for events moving at speeds less than 90 mswith respect to the ground a maximum slope of 150 mmkm boundsthe events observed Figure 13b shows the upper bound on themaximum gradient of this threat model as a function of satelliteelevation angle for speeds greater than 90 ms a slope of375 mm=km at low elevation increasing linearly at a rate of 1 mmkm from 15 to 65 deg elevation to reach 425 mm=km at highelevation For ground speeds below 90 ms the maximum slope is150mmkm independent of elevation These bounds slightly exceedthe largest gradients validated from the data analysis due to marginadded to account for measurement error In addition to the plottedmaximum gradient and speed bounds width (distance between highand low delay regions) is delimited to be between 25 and 200 km andtotal slant differential delay up to 50 m [2236]

Figure 13a shows a maximum estimated ionospheric front speedof about 600 m=s with respect to the ground and the speed was

limited to be nomore than 750 m=s This bound is based on a numberof considerations First in practice the absolute speed is of secondaryimportance to the front speedwith respect to the aircraft andLGF ray-paths (or effectively their IPPs) The relative motion permitsdetectability in fact the estimates of speed were very sensitive to thisrelative motion There is a real (albeit unlikely) possibility that IPPspeed might cancel out the actual ionospheric front speed relative tothe ground and thus prevent time-based LGF monitoring fromdetecting very large gradients for several minutes This possibilitysignificantly increases the maximum possible ionosphere-inducederror for LAAS users beyond what was initially projected in workthat did not consider this masking possibility (eg [37]) It alsoaffects the ionospheric anomaly simulation algorithms described in[2338] as the method for selecting the worst possible ionosphericfront from the point of view of a given satellite is now dictated by thetheoretical speed of the resulting IPP

The threat space shown in Fig 13 is used to represent whatthe ionosphere is potentially capable of during rare extreme-ionospheric-storm conditions based on the validated events observedin the ionospheric data analysis described in Sec V However inadopting this threat model for the present wemust recognize that thevalidated events that have occurred to date are few in number sincethey are driven by the most extreme storms that have impacted theAmerican sector and cover a limited geographical area withinCONUS due to constraints imposed by the CORS receiver sites andequipage It is possible that localized events severe enough topotentially threaten LAAS users have occurred in CONUS but arenot included in the data used to construct the threat space Thereforewhile this threat space is deemed final for the purposes of CAT ILAAS certification [39] the anomaly mitigation algorithms haveadjustable parameters in case future ionospheric storms or dataanalyses reveal gradients that fall outside this threat space Anotherpossibility is that better understanding of how the ionospherebehaves during storm events will allow us to reduce the conservatismin the model as well as develop a more physically accuratedescription of how ionospheric anomalies propagate through LGFand user observations

Users can be protected from these situations by inflating theprotection bound that is broadcast to exclude geometries that wouldlead to intolerably large positioning errors [2338] Even thoughthere is no evidence of such large gradients on nominal days withoutthe ability to detect ionospheric storms in real time that is possessedby WAAS LAAS must assume that the ionosphere may be in adisturbed state at all times As a result geometries that would beunsafe during a storm are disallowed even though severe storms arevery unlikely to be present thus sacrificing precision approachavailability One method for excluding geometries that achieves anacceptable level of availability is described in [23]

VII Conclusions

This paper represents the culmination of several years ofteamwork in conducting as comprehensive a search as possible forpast extreme ionospheric events inCONUS thatwould impact LAASuser safety We have identified the ionospheric threat to users ofLAAS parameterized a model to represent it and applied a two-phase method of ionospheric spatial-gradient analysis and validationto quantify the magnitude of these threats This method consists ofautomatic processing of dual-frequency GPS carrier-phase measure-ments of ionospheric delay combined with manual comparison tosingle-frequency code-carrier divergence measurements to searchfor an upper bound on possible anomalous ionospheric gradientsThe final threat space of the model is ultimately driven by the largestof these points found but the parameters of this model have gonethrough a number of revisions in magnitude throughout this processThese values represent our best-validated observations during whatare recognized within the ionospheric community as the mostextreme geomagnetic storms during and following the most recentsolar maximum

The most time-consuming aspect of ionospheric anomaly dataanalysis is manual review of the apparent anomalies output by the

Fig 13 Ionospheric gradient threat space anomalous ionospheric

gradients observed plotted in threat model space as a function of a) slope

and ground speed and b) elevation and slope

DATTA-BARUA ETAL 1149

automated-search software In themajority of cases it was evident tohuman eyes that receiver or postprocessing errors created an apparentionospheric gradient that was not real and these observations wereset aside However a significant number of events that could not bediscounted remained andwere subjected tomore detailed analysis byteam consensus

As a result of this work we have validated low-elevation spatialgradients in the ionospheric delay at L1 as high as 360 and413 mm=km at high elevation These observations were made withCORS network stations in northern Ohio during the 20 Nov 2003ionospheric storm Station redundancy rules out the possibility ofthese gradients being generated by faulty receivers or significanterrors in receiver bias estimation Simultaneous observations onother satellites at similar elevations were also unusually highTherefore we conclude that the largest gradients observed herewerenot due to nonionospheric causes

The impact of these gradients on WAAS precision approachintegrity is negligible due to the implementation of the WAASextreme storm detector (however WAAS precision approachavailability is dramatically affected when severe storms occur)Mitigating the safety threats posed by ionospheric storms to users ofLAAS is achieved by disallowing specific user satellite geometriesduring precision approach at the cost of somewhat reducing LAASCAT 1 availability

Acknowledgments

The authors thank the FAA LAAS Program Office for theirsupport in the completion of this effort We would also like to thankspecific individuals who helped us in this work including ToddWalter Juan Blanch and Shankar Ramakrishnan at Stanford JohnWarburton and Tom Dehel of the Federal Aviation AdministrationWilliam J Hughes Technical Center and Attila Komjathy of theNASA Jet Propulsion Laboratory

References

[1] Enge P ldquoLocal AreaAugmentation ofGPS for the PrecisionApproachof Aircraftrdquo Proceedings of the IEEE Vol 87 No 1 Jan 1999pp 111ndash132doi1011095736345

[2] Enge P Walter T Pullen S Kee C Chao Y C and Tsai Y JldquoWide Area Augmentation of the Global Positioning SystemrdquoProceedings of the IEEE Vol 84 No 8 Aug 1996 pp 1063ndash1088doi1011095533954

[3] Misra P and Enge P Global Positioning System Signals

Measurement and Performance 2nd ed GangandashJamuna LincolnMA 2006

[4] Tascione T F Introduction to the Space Environment KriegerMalabar FL 1994

[5] Klobuchar J ldquoIonospheric Effects on GPSrdquo Global Positioning

System Theory and Applications edited by B W Parkinson and J JSpilker Vol 1 Progress in Astronautics and Aeronautics AIAAWashington DC 1996 pp 485ndash515

[6] Datta-Barua S Mannucci A J Walter T and Enge P ldquoAltitudinalVariation of Midlatitude Localized TEC Enhancement from Ground-and Space-BasedMeasurementsrdquo Space Weather Vol 6 No S10D062008doi1010292008SW000396

[7] Roble R G and Ridley E C ldquoThermospherendashIonospherendashMesosphere Electrodynamics General Circulation Model (TIME-GCM) Equinox Solar Cycle Minimum Simulations (30ndash500 km)rdquoGeophysical Research Letters Vol 21 No 6 1994 pp 417ndash420doi10102993GL03391

[8] Crowley G et al ldquoGlobal ThermospherendashIonosphere Response toOnset of 20 November 2003Magnetic Stormrdquo Journal of GeophysicalResearch Vol 111 No A10S18 2006doi1010292005JA011518

[9] Huba J D Joyce G and Fedder J A ldquoSami2 is Another Model ofthe Ionosphere (SAMI2) A New Low-Latitude Ionosphere ModelrdquoJournal of Geophysical Research Vol 105 No A10 2000 pp 23035ndash23053doi1010292000JA000035

[10] Bilitza D ldquoInternational Reference Ionosphere 2000rdquo Radio ScienceVol 36 No 2 MarchndashApril 2001 pp 261ndash275

doi1010292000RS002432[11] PiXWangC Hajj GA RosenGWilson BD andBaileyG J

ldquoEstimation of ExB Drift Using a Global Assimilative IonosphericModel An Observation System Simulation Experimentrdquo Journal of

Geophysical Research Vol 108(A2) No 1075 2003doi1010292001JA009235

[12] Schunk R et al ldquoGlobal Assimilation of Ionospheric Measurements(GAIM)rdquo Radio Science Vol 39 No RS1S02 2004doi1010292002RS002794

[13] Bust G S et al ldquoFour-Dimensional GPS Imaging of Space WeatherStormsrdquo Space Weather Vol 5 No S02003 2007doi1010292006SW000237

[14] Klobuchar J A Doherty P H and El-Arini M B ldquoPotentialIonospheric Limitations to Wide-Area Differential GPSrdquo Proceedingsof the 16th International Technical Meeting of the Satellite Division of

The Institute Of Navigation (IONGPS 1993) Salt Lake City UT 1993pp 1245ndash1254

[15] Klobuchar J A and Kunches J ldquoEye on the Ionosphere The SpatialVariability of Ionospheric Range Delayrdquo GPS Solutions Vol 3 No 32000 pp 70ndash74doi101007PL00012808

[16] Walter T et al ldquoRobust Detection of Ionospheric IrregularitiesrdquoProceedings of IONGPS 2000 httpwaasstanfordedu~wwupapersgpsPDFtoddion00pdf Salt Lake City UT 2000 pp 209ndash218

[17] Christie J R I Ko P-Y Hansen A Dai D Pullen S Pervan Band Parkinson B ldquoThe Effects of Local Ionospheric Decorrelation onLAAS Theory and Experimental Resultsrdquo Proceedings of the Instituteof Navigation National Technical Meeting 1999 pp 769ndash777

[18] ldquoMinimum Operational Performance Standards for GPS Local AreaAugmentation System Airborne Equipmentrdquo Radio TechnicalCommission for Aeronautics Washington DC Publ DO-253C2008

[19] Lee J Pullen S Datta-Barua S and Enge P ldquoAssessment ofNominal Ionosphere Spatial Decorrelation for GPS-Based AircraftLanding Systemsrdquo Journal of Aircraft Vol 44 No 5 2007 pp 1662ndash1669doi102514128199

[20] Datta-Barua SWalter T Pullen S LuoM Blanch J and Enge PldquoUsing WAAS Ionospheric Data to Estimate LAAS Short BaselineGradientsrdquo Proceedings of the Institute of Navigation National

Technical Meeting 2002 pp 523ndash530[21] Luo M Pullen S Akos D Xie G Datta-Barua S Walter T and

Enge P ldquoAssessment of Ionospheric Impact on LAAS Using WAASSupertruth Datardquo Proceedings of the ION 58th Annual Meeting and

CIGTF 21st Guidance Test Symposium 2002 pp 175ndash186[22] Ene A Qiu D Luo M Pullen S and Enge P ldquoAComprehensive

Ionosphere Storm Data Analysis Method to Support LAAS ThreatModel Developmentrdquo Proceedings of the Institute of Navigation

National Technical Meeting 2005 pp 110ndash130[23] Ramakrishnan S Lee J Pullen S andEnge P ldquoTargetedEphemeris

Decorrelation Parameter Inflation for Improved LAAS AvailabilityDuring Severe Ionosphere Anomaliesrdquo Proceedings of the 2008

National TechnicalMeeting of the Institute of Navigation pp 354ndash366[24] Komjathy A L Sparks and A J Mannucci ldquoGenerating High

Precision Ionospheric Ground-Truth Measurementsrdquo US PatentNo 7289061 B2 filed 30 Oct 2007

[25] Datta-Barua S ldquoIonospheric Threats to the Integrity of Airborne GPSUsersrdquo PhD Dissertation Stanford Univ Palo Alto CA 2008

[26] Luo M Pullen S Dennis J Konno H Xie G Walter T Enge PDatta-Barua S and Dehel T ldquoLAAS Ionosphere Spatial GradientThreat Model and Impact of LGF and Airborne MonitoringrdquoProceedings of the 16th International TechnicalMeeting of the Satellite

Division of the Institute of Navigation IONGPSGNSS 2003 pp 2255ndash2274

[27] Luo M Pullen S Walter T and Enge P ldquoIonosphere SpatialGradient Threat for LAAS Mitigation and Tolerable Threat SpacerdquoProceedings of the 2004 National Technical Meeting of the Institute of

Navigation pp 490ndash501[28] National Geodetic Survey CORS Team ldquoCORS Continuously

Operating Reference Stationsrdquo httpwwwngsnoaagovCORS[retrieved 6 Jan 2009]

[29] National Geophysical Data Center ldquoSpace Physics Interactive DataResourcerdquo 2005 httpspidrngdcnoaagovspidr [retrieved1 March 2005]

[30] Sugiura M and Kamei T ldquoEquatorial Dst Index 1957ndash1986rdquoInternational Association of Geomagnetism and Aeronomy Vol 401991 pp 7ndash14

[31] Menvielle M and Berthelier A ldquoThe K-Derived Planetary IndicesDescription and Availabilityrdquo Reviews of Geophysics Vol 29 No 3

1150 DATTA-BARUA ETAL

As noted above the gradient is assumed to represent a linearchange in slant ionospheric delay between maximum and minimumdelays As a result the bounding slant gradients are also (at leastpotentially) functions of satellite elevation

To recap this threat model assumes severe ionospheric spatialgradients take the form of straight horizontally semi-infinite wavefronts of linearly varying TEC that move at constant speed anddirectionwith respect to the ground In addition the slope of the rampitself is not time-varying These are simplifications of reality but arereasonable for the duration of an aircraft approach These assump-tions are adequate because deviations from them do not materiallychange the final result except if most-evil variations (compoundingthe worst-case upon the worst-case) are allowed

The ionospheric threat model does not include other parametersthat are necessary for simulating and assessing impact on LAAS-equipped aircraft such as the orientation of the ionospheric wavefront with respect to the aircraft and runway the horizontal approachspeed of the aircraft the approach direction of the ionospheric frontthe initial position of the ionospheric front with respect to the aircraftat the start of the simulation and the velocities of the IPPs which aredriven by satellite and receiver motion Figure 1 illustrates anexample of one possible configuration of the ionospheric threatmodeluserrunwaysatellite scenario

IV Data

The remaining sections of this paper are concerned withidentifying theworst observed values of the ionospheric threatmodelparameters of slope and speed at different ranges of elevation anglesExisting ionospheric models do not capture anomalous behaviorsuch as that observed on 6 April 2000 so our threat-space devel-opment for the ionosphere is data-driven Precise estimates ofionospheric delays can be obtained using dual-frequency GPSmeasurements from networks of stations and sophisticated post-processing algorithms

The US National Geodetic Survey makes dual-frequency GPSdata publicly available in RINEX format from a network ofcontinuously operating reference stations (CORS) [28] CORS hashundreds of stations in CONUS (sites as of 29 Oct 2003 are shownin Fig 3) and permits ionospheric measurement comparisons acrossbaselines of tens of kilometers Before analysis geographicalclusters of the highest-density receiver regions in the United Statesare targeted to provide the best possible spatial resolution Eightclusters overCONUSof around 21 square degrees of average area aredefined and boxed in Fig 3 Each of these clusters contains 10ndash30receivers with baseline separations of 15ndash200 km Most of the dataanalysis is performed for stations inside these clusters In some casesadditional stations adjacent to these clusters are also used to bettercapture ionospheric wave-front behavior that extended beyondcluster boundaries

The dates fromwhich data were collected and analyzed are shownin Table 1 along with the publicly available indices of globalgeomagnetic activity planetary K (Kp) and disturbance storm time(Dst) and geomagnetic storm class (G-class) and WAAS coverageParticular geographic regions of focus if applicable are shown

The values of Kp Dst G-class and WAAS coverage are used tohelp target days onwhich anomalous behavior in CONUSwas likelyto have occurred Because the ionosphere is coupled to themagnetosphere which is in turn driven by the solar wind measuresof geomagnetic activity provide information about the ionosphere byproxy The Dst index measures activity in the worldwide geomag-netic field [2930] with negative deviations indicating ionosphericcurrents and activity Kp is a three-hour composite index based ongeomagnetic activity measured at several midlatitude stationsprimarily located in the northern hemisphere [3132] Kp ranges inthirds of an index unit from 0 (no activity) to 9 (extreme activity) TheSpace Weather Prediction Center of the National Oceanic andAtmospheric Administration has developed a geomagnetic stormscale and associated descriptors for use by both the technicalcommunity and the general public [33] The scale varies from G1throughG5 and the storm classes associated with each level in orderof increasing intensity are minor moderate strong severe andextreme

The percentage of WAAS coverage indicates the degree to whichthese storms introduced nonlinear spatial variations over CONUS(viewed over separations of hundreds of km between WAASreference stations) limiting WAAS availability for precision ap-proach While the other measures shown in Table 1 are globalWAAS coverage is a daily summary value that indicates how muchimpact each storm had in the American sector where the CORSstations are sited For the dates occurring before WAAS initialoperating capability (IOC) official coverage percentages do not exist

The data collected from the CORS network have been assimilatedand had biases removed by the use of the global ionosphericmapping(GIM) software atNASArsquos Jet PropulsionLaboratory (JPL)With theadditional use of the publicly available International GNSS Servicestation data worldwide GIM estimates and removes satellite andreceiver interfrequency biases to provide high-precision ionosphericmeasurements The dual-frequency ionospheric observables IDF arecomputed by leveling the phase estimates I using the codeestimates I The processing with a leveling function f is describedin detail in [34] The resulting data are provided at 30 s intervals

IDF fI I (6)

CORS ionospheric estimates are processed in a similar manner tothose ofWAAS (see Sec II) but the lack ofmultiple receivers at eachsite means that anomalies due to receiver glitches cannot be votedout In addition the quality of CORS receiver equipage and siting ismuch more variable than that of WAAS reference stations thustypical CORS receiver measurement errors are several times higherthan that of WAAS receivers The screening algorithms used at JPLare based on an error detection threshold that was set to allow moredata points and minimize the risk of discarding true ionosphericevents

V Data Analysis Procedure

The objective of this section is to produce the LAAS threat spaceon parameters g and v using the data from closely-spaced CORSstations described above The procedure for processing CORS

Table 1 Ionospheric storm dates and conditions investigated

Day (UT mmddyy) Kp Dst Geomagneticstorm class

WAAS coverage Focus region

040600 83 287 Severe None (pre-IOC) NE corridor040700 87 288 Extreme None (pre-IOC) NE corridor071500 90 289 Extreme None (pre-IOC) mdashmdash

071600 77 301 Strong None (pre-IOC) mdashmdash

090702 73 163 Strong None (pre-IOC) mdashmdash

102903 90 345 Extreme 0 mdashmdash

103003 90 401 Extreme 0 TXndashOKndashLAndashAR103103 83 320 Severe 0 FLndashGA112003 87 472 Extreme 0 OHndashMI071704 60 80 Moderate 688 TXndashOKndashLAndashAR

DATTA-BARUA ETAL 1145

receiver data identifying anomalously high spatial rates of changevalidating anomalies that are due to ionospheric events andestimating parameters in terms of the threatmodel of Sec III is shownin Fig 6 The starting point is raw L1 and L2 measurements fromselected clusters of CORS stations An automated algorithm flowingdown the left-hand side of the figure involves using the JPL-processed CORS-based estimates of ionospheric delay to computeapparent spatial gradients and front speeds and screen them forobvious receiver or data-processing errors The remaining points arecompared manually (right-hand side of Fig 6) to estimates derivedfrom raw CORS data The following sections trace through themethodology outlined in the flowchart in Fig 6

A Ionospheric Spatial-Gradient Estimation

1 Station Pair Method

Our approach involves considering selected pairs of CORSstations in the JPL-processed database as though they represent anLGF-user receiver pair (with a static user position) This techniquecalled the station pair method is illustrated in Fig 7 For each epocht the delays at each of two stations i and j viewing the same satellite kare differenced The slope of ionospheric delayrI between all pairsof receivers looking at each satellite is estimated by dividing thedifferential slant delay by the baseline distance between the tworeceivers

rIt jIki t Ikj tjkxi xjk

(7)

The station pair method unlike the time-step method (Sec II)does not artificially inflate ionospheric gradients due to relativemotions of IPPs and the ionosphere [19]

The dual-frequency data in each CORS cluster are separated intofive elevation bins 0ndash12 12ndash20 20ndash30 30ndash45 and 45ndash90 deg Foreach LOS elevation range receivers in each cluster are grouped intopairs and IDF is used for I in Eq (7) to give a dual-frequencyestimate of the gradient rIDF

2 Automated Screening

AfterrIDF is computed an automated screening process (bottom-left corner of Fig 6) isolates the most credible events above a chosenthreshold (100 mm=km) and eliminates those gradients that areapparently due to nonionospheric causes Cases for which tworeceivers are effectively collocated (less than 100m apart) or the slantgradient rIDF does not vary in time are attributed to an interreceiverbias and eliminated Cases for which ionospheric delay measure-ments from one receiver do not vary in time are attributed to a faultyreceiver and eliminated

Variations greater than 15 mm=s ionospheric delay rate of changeare flagged for possible ionospheric activity The 15 mm=s thresholdis set to be an order of magnitude lower than the largest time rate ofchange observed for the 29 Oct 2003 storm day [35] in order toallow significant rapid changes in the delay value to pass screening aspossible ionospheric events The advantage of this threshold is thatreal ionospheric events are less likely to be eliminated as falsenegatives The tradeoff is that false alarms occur when the datacontain cycle slips causing the delay value to jump suddenlybetween two successive measurement points These cases have to beeliminated manually from the screening results

3 Manual Validation

The plausible ionospheric events that survive the computer-automated screening process are analyzed manually For each ofthese events visual analysis determineswhether themagnitude of thegradients together with the length of time that these gradientsmanifest themselves look reasonable or aremore likely to be artifactsof faulty receiver tracking

The first step in verifying an event is by comparison againstsimultaneous observations of slant delay from other nearby receiversin the same cluster This can be thought of as cluster-wide voting thatoperates in a similar manner to the voting among colocated receiversat each WAAS reference station If other nearby CORS receiversexhibit similar ionospheric delay patterns to the pair that shows themost-extreme spatial decorrelation the event is very probably realand is not limited to a single receiver fault On the other hand if theobservations were influenced by receiver artifacts in the data it isunlikely that amajority of the receivers in the cluster contain the sameor similar artifacts

To support this comparison plots are constructed that extendacross the entire period of time for which data was extracted so thatthe receiver behavior can be analyzed both in the presence ofdisturbed ionosphere and also during calm ionospheric conditions oneither side of the disturbance While the electron content within theionosphere is expected to vary wildly during the actual storm delaymeasurements from the different receivers within a CORS cluster areexpected to converge to similar values at times of nominalionospheric activity Quiet periods of ionospheric behavior help toestablish a baseline for the affected receiver cluster and give anindication of any possible interreceiver biases

The next step in validating extreme gradients (top-right corner ofFig 6) is to estimate approximate wave-front parameters from rawL1 code-minus-carrier measurements ICMC and compare them to theobservations from the JPL-processed dual-frequency data IDF TheL1-only estimates of these slopesrICMC given by using ICMC for theslant delay I in Eq (7) provide a picture of gradients that is notsubject to fragile L2 tracking loops The L1-only estimates are notexact however because the integer ambiguity N1 has not beeneliminated

Two methods are used to remove the integer ambiguity for high-elevation and low-elevation LOSs respectively For high elevationthe postprocessed L1L2 estimate of absolute differential sloperIDFat a timewell before or after thewave front passes is used to initializethe single-frequency ionospheric gradient estimates rICMC A pointin time is picked at which the measurements rICMC are aligned torIDF by subtracting a constant bias from all the measurement of oneof the receivers equalizing the value that they measure at the choseninstant Since the differential delay between a pair of receivers isobserved this is equivalent to choosing a reference zero-slope point

L1 code and carrier

L1 code and carrier L2 carrier

ldquoRawrdquo IGSCORS Data

JPL Ionosphere ldquoTruthrdquo Processing

Ionosphere Delay Estimates

Find Maximum Apparent Gradients

(Station Pair Method)

Screening Process (Automated)

Database of Extreme Gradients Erroneous Receiver

Steps and L1L2 Biases Removed Investigate

Remaining Points

Remove ldquoQuestionablerdquoObservations

Estimate Gradients from L1 code-minus-carrier

Output Database of Maximum Gradients

Compare L1L2 and L1 CMC Gradient Obs

ldquoValidatedrdquoMax Gradient

Database

Fig 6 Ionospheric anomaly data analysis procedure

Fig 7 Station pair method Not to scale

1146 DATTA-BARUA ETAL

in rICMC at a particular instant in time and then expressing all otherslope measurements relative to it Thus L1 CMC ionosphericgradient estimates form lower bounds on the true gradient but theseestimates are accurate enough that they can be used to validate theL1L2 observations

For low-elevation satellites the track may not be long enough tosample through quiet ionosphere In this case the single-frequencygradient estimate is leveled to the mean of the JPL-processed dual-frequency estimate rIDF as shown in Eq (8)

r ~ICMCt rICMCt rICMC rIDF (8)

Anomalous ionospheric events not eliminated by this final manual

comparison to single-frequency estimates r ~ICMC of the spatialvariation are deemed to be validated measurements rIDFValidated andare used to generate bounds on the threat model parameter g

gmaxtrIDFValidated (9)

B Ionospheric Front Speed Estimation

Observations of severe gradients that are validated as described inSec VA are studied inmore detail to characterize them in the contextof the ionospheric threatmodel geometry The automated code used athree-station-based trigonometric algorithm to estimate the speedand direction of wave-front propagation with respect to the ground(see [22] for details) The travel time of the frontwhich swept a pair ofstations is measured to compute the speed and the third station isused to observe the front direction This algorithm proved to be verysensitive to measurement errors and limitations of the threat model(in particular the assumption that the wave front is a straight line)when extrapolated to cover three (instead of two) stations

To overcome the weakness of the automated ionospheric frontspeed computation method a manual two-station-based method isused that takes wave-front orientation (estimated manually based onmany stations) as an input so that only speed needs to be solved forFirst we form groups that typically include four to eight nearbyCORS receiverswhich show similar ionospheric delay patterns to thesatellite-receiver pair from which anomalous gradients wereobserved and verified By observing the time history of ionosphericslant delays from the stations observing the same satellite the peakdelay times tpeaki at which the slant delay of the station i reached thelocal maximum value are determined Because ionospheric delays ofthese nearby stations exhibit similar patterns the order in which thestations were affected by the ionospheric front can be deduced fromtpeaki This order and the locations of the affected stations make itpossible to approximately determine the forward propagationdirection of the front Since it was assumed that the wave front is astraight line and moves with constant speed which is not true inreality the sequence and tpeaki must be carefully adjusted manuallyAlso the slant delays sometimes appear to have more than one localmaximum and this ambiguity makes room for tpeaki to be fine-tunedconsidering the underlying assumptions

Next we select a pair of stations i j and determine the traveltime tij given by Eq (10)

tijinej tpeaki tpeakj i j 1 2 N (10)

The distance between the two stations projected onto the lineperpendicular to the wave frontdn is used to compute the speed ofthe wave front vn as shown in Eq (11)

vn dn

tijinej(11)

Note that this speed estimate contains the velocity componentresulting from the movement of the satellite in addition to that fromactual wave-front motion Thus the (notional) IPP velocity iscomputed and removed so that the resulting wave-front velocityestimates are referenced to a fixed point on the ground (such as anLGF site) IPP velocity and direction are computed from the CORS

data which give the notional IPP locations for each satellite at 30 sintervals The purpose of using IPP velocity even though the IPPconcept is not otherwise used in defining this threat model is tocapture in an average sense the speed of the ray-path motion throughthe ionosphere We repeat this computation for all combinations ofstations and obtain a range of front-speed estimates that are normal tothe ionospheric front Finally we take themean value of the resultingspeed estimates which is the manually estimated and validatedwave-front velocity with respect to the ground

VI Results CONUS Ionospheric AnomalyThreat Model

In this section we analyze two case studies following the processdescribed in Sec V These two cases are of particular interest becausethey give the maximum dual-frequency verified slopes we observedat high elevation (Sec VIA) and at low elevation (Sec VIB)respectively Both cases were observed from the OhioMichigancluster shown in the inset of Fig 3 and show very weak elevationdependence We then summarize the resulting threat model forCONUS

A Case Study 1 High Elevation

Figure 8 shows the slant ionospheric delay from this event asobserved by seven CORS reference stations in northern Ohio andsouthern Michigan as they tracked GPS SVN 38 Note the rapidgrowth in delay associatedwith the passage of the leading edge of theplume of enhanced delay shown in Fig 2 in just under an hourfollowed by an interval of erratic variation in ionospheric delaywithin the plumewhile the overall delay remains high followed by asudden steep drop-off corresponding to the duskward edge of theplume The JPL-processed dual-frequency delays (Fig 9a) andelevation angles (Fig 9c) over time are shown for ZOB1 (red) andGARF (green) which are separated by only 512 km The dual-frequency estimate of the sloperIDF between these stations is shownin Fig 9b

Data outages on both ZOB1rsquos and GARFrsquos dual-frequencymeasurements are visible in Fig 9a The drop from 30 to 18 m on theGARF (green) curve at 2055UTand on ZOB1 (red) at 2100 UT callsinto question the reliability of the dual-frequency estimate of theslope For this reason we validate the presence of an actual iono-spheric spatial anomaly within this data by comparing rIDF (blue)with the single-frequency estimate of the slope rICMC (red) inFig 10 The data outages present from 2055 to 2100 UT in rIDF donot exist in the L1-only measurements confirming that the data gapsare limited to L2measurements The general trend over time betweenthe two slope measurements agrees including the time during whichL2 data outages occur Based on the similarity of this event toobservations (Fig 8) by other receivers in the vicinity we can

Fig 8 Dual-frequency estimates of slant ionospheric delay from JPL-

processed CORS data vs UT hour for select stations in the Ohio

Michigan cluster tracking SVN 38 at high elevation

DATTA-BARUA ETAL 1147

confirm amaximum slope observed due to this event of 413 mm=kmat 2100 UT

B Case Study 2 Low Elevation

We also investigate data to show the largest-magnitude ion-ospheric gradients that we have validated at low elevation (less than15 deg) at 2130 UT Figure 11 is a plot of the dual-frequencymeasurements of slant ionospheric delay over time at these stationswhen observing SVN 26 to the northwest at about 12 deg elevation(not shown) The two stations FREO and WOOS (see inset of Fig 3for location) begin at 15 m and rise to over 45 m of slant delay as the

LOSs from these stations to SVN 26 pass through the filament ofenhanced ionospheric delay

We analyze the WOOSndashGARF pair of stations to estimate theionospheric spatial gradient between them The dual-frequency-based slope rIDF is plotted in blue on Fig 12 The slope rises from0 mm=km to nearly 400 mm=km as the delay for WOOS rises to45 m during passage through the anomalous region while GARFholds steady at about 15ndash18m There is a dual-frequency data gap forGARF at 2054 UT that contributes to the data gap inrIDF howeverthe outage is not simultaneous with the highest slope observedbetween this station pair

The dual-frequency estimate is compared with the single-frequency estimate of the ionospheric spatial gradientrICMC (greenon Fig 12) Continuous single-frequencymeasurements exist exceptat one epoch at 2052 UT at which time the L1 measurements had avisible cycle slip that was manually removed Based on theagreement between the two measurements we conclude that thehighest slope between the stationsWOOS and GARF occurs at 2120UT and is about 360 mm=km

C LAAS Ionospheric Threat Space

Using the method described in Sec V and illustrated in thesubsections above numerous other instances of high differentialdelay between receivers closer than 100 km were investigated frommultiple storm days in CONUS listed in Table 1 This was amultistage multiperson process spanning data from storms from

20 202 204 206 208 21 212 214 216 218 220

10

20

30

40Dual-frequencyZOB1 GARF SV38

Sla

nt

del

ay (

m)

ZOB1

GARF

20 202 204 206 208 21 212 214 216 218 22-200

0

200

400

600

Ion

o s

lop

e (m

mk

m)

20 202 204 206 208 21 212 214 216 218 2240

50

60

70

80

Ele

vati

on

(d

eg)

Time (hour of 11202003)

a)

b)

c)

Fig 9 Maximum ionospheric gradient observed at high elevation

a) dual-frequency estimates of slant ionospheric delay b) spatial rate of

change and c) elevation from JPL-processed CORS data vs UT hour for

receivers ZOB1 and GARF tracking SVN 38 at high elevation

20 202 204 206 208 21 212 214 216 218 22-50

0

50

100

150

200

250

300

350

400

450

Time (hour of 11202003)

Ion

o S

lop

e (m

mk

m)

ZOB1 and GARF Slopes Comparison SV38

DF Slope

L1 Slope

Fig 10 Spatial rate of change of ionospheric delay over time between

two stations in OhioMichigan cluster viewing SVN 38 at high elevation

from rIDF (blue) and rICMC (red)

207 208 209 21 211 212 213 2140

5

10

15

20

25

30

35

40

45

50Iono Delay for Selected Stations SV 26 Elevation100689ordm - 12078ordm

Time(Hour of 11202003)

Sla

nt Io

no D

elay

(m

)

FREO

WOOS

LSBN

GARFGUST

Fig 11 Dual-frequency estimates of slant ionospheric delay from JPL-processed CORS data vs UT hour for select stations in the Ohio

Michigan cluster tracking SVN 26 at low elevation

208 209 21 211 212 213 214-50

0

50

100

150

200

250

300

350

400

Time (hour of 11202003)

Ion

o S

lop

e (m

mk

m)

WOOS and GARF Slopes Comparison SV26

DF Slope

L1 Slope

Fig 12 Spatial rate of change of ionospheric delay over time between

two stations inOhioMichigan cluster viewing SVN26at lowelevation as

from rIDF (blue) and rICMC (green)

1148 DATTA-BARUA ETAL

2000ndash2005 during the maximum and waning phases of the mostrecent solar cycle

Figure 13 summarizes the results of this data analysis process bypopulating the threat space with cases observed in CORS data fromthe storms examined In Fig 13a the instances of anomalousgradients are plotted as a function of slope (mm=km) on thehorizontal axis and ground speed (m=s) on the vertical axis Thepoints that were identified in dual-frequency data and subsequentlyverified with single-frequency L1 code-minus-carrier data aremarked with diamonds The points that were estimated from therICMC directly are shown as triangles These same points are plottedin Fig 13b as a function of elevation angle (degrees) on the horizontalaxis and slope (mm=km) on the vertical axis

Note that width estimates do not appear in this figure Thewidth ofthe TEC rampwas generally difficult to assess from the available datagiven the typical 50ndash100 km separation of CORS reference stationsIn addition the integrity results (ie the threat model impacts onLAAS users) were not found to be sensitive to this parameter [27]

Empirical bounds are fitted to the points shown in Fig 13 in orderto limit the threat space to the maximum gradients discovered andvalidated These bounds are marked with solid lines in Fig 13Figure 13a shows that for events moving at speeds less than 90 mswith respect to the ground a maximum slope of 150 mmkm boundsthe events observed Figure 13b shows the upper bound on themaximum gradient of this threat model as a function of satelliteelevation angle for speeds greater than 90 ms a slope of375 mm=km at low elevation increasing linearly at a rate of 1 mmkm from 15 to 65 deg elevation to reach 425 mm=km at highelevation For ground speeds below 90 ms the maximum slope is150mmkm independent of elevation These bounds slightly exceedthe largest gradients validated from the data analysis due to marginadded to account for measurement error In addition to the plottedmaximum gradient and speed bounds width (distance between highand low delay regions) is delimited to be between 25 and 200 km andtotal slant differential delay up to 50 m [2236]

Figure 13a shows a maximum estimated ionospheric front speedof about 600 m=s with respect to the ground and the speed was

limited to be nomore than 750 m=s This bound is based on a numberof considerations First in practice the absolute speed is of secondaryimportance to the front speedwith respect to the aircraft andLGF ray-paths (or effectively their IPPs) The relative motion permitsdetectability in fact the estimates of speed were very sensitive to thisrelative motion There is a real (albeit unlikely) possibility that IPPspeed might cancel out the actual ionospheric front speed relative tothe ground and thus prevent time-based LGF monitoring fromdetecting very large gradients for several minutes This possibilitysignificantly increases the maximum possible ionosphere-inducederror for LAAS users beyond what was initially projected in workthat did not consider this masking possibility (eg [37]) It alsoaffects the ionospheric anomaly simulation algorithms described in[2338] as the method for selecting the worst possible ionosphericfront from the point of view of a given satellite is now dictated by thetheoretical speed of the resulting IPP

The threat space shown in Fig 13 is used to represent whatthe ionosphere is potentially capable of during rare extreme-ionospheric-storm conditions based on the validated events observedin the ionospheric data analysis described in Sec V However inadopting this threat model for the present wemust recognize that thevalidated events that have occurred to date are few in number sincethey are driven by the most extreme storms that have impacted theAmerican sector and cover a limited geographical area withinCONUS due to constraints imposed by the CORS receiver sites andequipage It is possible that localized events severe enough topotentially threaten LAAS users have occurred in CONUS but arenot included in the data used to construct the threat space Thereforewhile this threat space is deemed final for the purposes of CAT ILAAS certification [39] the anomaly mitigation algorithms haveadjustable parameters in case future ionospheric storms or dataanalyses reveal gradients that fall outside this threat space Anotherpossibility is that better understanding of how the ionospherebehaves during storm events will allow us to reduce the conservatismin the model as well as develop a more physically accuratedescription of how ionospheric anomalies propagate through LGFand user observations

Users can be protected from these situations by inflating theprotection bound that is broadcast to exclude geometries that wouldlead to intolerably large positioning errors [2338] Even thoughthere is no evidence of such large gradients on nominal days withoutthe ability to detect ionospheric storms in real time that is possessedby WAAS LAAS must assume that the ionosphere may be in adisturbed state at all times As a result geometries that would beunsafe during a storm are disallowed even though severe storms arevery unlikely to be present thus sacrificing precision approachavailability One method for excluding geometries that achieves anacceptable level of availability is described in [23]

VII Conclusions

This paper represents the culmination of several years ofteamwork in conducting as comprehensive a search as possible forpast extreme ionospheric events inCONUS thatwould impact LAASuser safety We have identified the ionospheric threat to users ofLAAS parameterized a model to represent it and applied a two-phase method of ionospheric spatial-gradient analysis and validationto quantify the magnitude of these threats This method consists ofautomatic processing of dual-frequency GPS carrier-phase measure-ments of ionospheric delay combined with manual comparison tosingle-frequency code-carrier divergence measurements to searchfor an upper bound on possible anomalous ionospheric gradientsThe final threat space of the model is ultimately driven by the largestof these points found but the parameters of this model have gonethrough a number of revisions in magnitude throughout this processThese values represent our best-validated observations during whatare recognized within the ionospheric community as the mostextreme geomagnetic storms during and following the most recentsolar maximum

The most time-consuming aspect of ionospheric anomaly dataanalysis is manual review of the apparent anomalies output by the

Fig 13 Ionospheric gradient threat space anomalous ionospheric

gradients observed plotted in threat model space as a function of a) slope

and ground speed and b) elevation and slope

DATTA-BARUA ETAL 1149

automated-search software In themajority of cases it was evident tohuman eyes that receiver or postprocessing errors created an apparentionospheric gradient that was not real and these observations wereset aside However a significant number of events that could not bediscounted remained andwere subjected tomore detailed analysis byteam consensus

As a result of this work we have validated low-elevation spatialgradients in the ionospheric delay at L1 as high as 360 and413 mm=km at high elevation These observations were made withCORS network stations in northern Ohio during the 20 Nov 2003ionospheric storm Station redundancy rules out the possibility ofthese gradients being generated by faulty receivers or significanterrors in receiver bias estimation Simultaneous observations onother satellites at similar elevations were also unusually highTherefore we conclude that the largest gradients observed herewerenot due to nonionospheric causes

The impact of these gradients on WAAS precision approachintegrity is negligible due to the implementation of the WAASextreme storm detector (however WAAS precision approachavailability is dramatically affected when severe storms occur)Mitigating the safety threats posed by ionospheric storms to users ofLAAS is achieved by disallowing specific user satellite geometriesduring precision approach at the cost of somewhat reducing LAASCAT 1 availability

Acknowledgments

The authors thank the FAA LAAS Program Office for theirsupport in the completion of this effort We would also like to thankspecific individuals who helped us in this work including ToddWalter Juan Blanch and Shankar Ramakrishnan at Stanford JohnWarburton and Tom Dehel of the Federal Aviation AdministrationWilliam J Hughes Technical Center and Attila Komjathy of theNASA Jet Propulsion Laboratory

References

[1] Enge P ldquoLocal AreaAugmentation ofGPS for the PrecisionApproachof Aircraftrdquo Proceedings of the IEEE Vol 87 No 1 Jan 1999pp 111ndash132doi1011095736345

[2] Enge P Walter T Pullen S Kee C Chao Y C and Tsai Y JldquoWide Area Augmentation of the Global Positioning SystemrdquoProceedings of the IEEE Vol 84 No 8 Aug 1996 pp 1063ndash1088doi1011095533954

[3] Misra P and Enge P Global Positioning System Signals

Measurement and Performance 2nd ed GangandashJamuna LincolnMA 2006

[4] Tascione T F Introduction to the Space Environment KriegerMalabar FL 1994

[5] Klobuchar J ldquoIonospheric Effects on GPSrdquo Global Positioning

System Theory and Applications edited by B W Parkinson and J JSpilker Vol 1 Progress in Astronautics and Aeronautics AIAAWashington DC 1996 pp 485ndash515

[6] Datta-Barua S Mannucci A J Walter T and Enge P ldquoAltitudinalVariation of Midlatitude Localized TEC Enhancement from Ground-and Space-BasedMeasurementsrdquo Space Weather Vol 6 No S10D062008doi1010292008SW000396

[7] Roble R G and Ridley E C ldquoThermospherendashIonospherendashMesosphere Electrodynamics General Circulation Model (TIME-GCM) Equinox Solar Cycle Minimum Simulations (30ndash500 km)rdquoGeophysical Research Letters Vol 21 No 6 1994 pp 417ndash420doi10102993GL03391

[8] Crowley G et al ldquoGlobal ThermospherendashIonosphere Response toOnset of 20 November 2003Magnetic Stormrdquo Journal of GeophysicalResearch Vol 111 No A10S18 2006doi1010292005JA011518

[9] Huba J D Joyce G and Fedder J A ldquoSami2 is Another Model ofthe Ionosphere (SAMI2) A New Low-Latitude Ionosphere ModelrdquoJournal of Geophysical Research Vol 105 No A10 2000 pp 23035ndash23053doi1010292000JA000035

[10] Bilitza D ldquoInternational Reference Ionosphere 2000rdquo Radio ScienceVol 36 No 2 MarchndashApril 2001 pp 261ndash275

doi1010292000RS002432[11] PiXWangC Hajj GA RosenGWilson BD andBaileyG J

ldquoEstimation of ExB Drift Using a Global Assimilative IonosphericModel An Observation System Simulation Experimentrdquo Journal of

Geophysical Research Vol 108(A2) No 1075 2003doi1010292001JA009235

[12] Schunk R et al ldquoGlobal Assimilation of Ionospheric Measurements(GAIM)rdquo Radio Science Vol 39 No RS1S02 2004doi1010292002RS002794

[13] Bust G S et al ldquoFour-Dimensional GPS Imaging of Space WeatherStormsrdquo Space Weather Vol 5 No S02003 2007doi1010292006SW000237

[14] Klobuchar J A Doherty P H and El-Arini M B ldquoPotentialIonospheric Limitations to Wide-Area Differential GPSrdquo Proceedingsof the 16th International Technical Meeting of the Satellite Division of

The Institute Of Navigation (IONGPS 1993) Salt Lake City UT 1993pp 1245ndash1254

[15] Klobuchar J A and Kunches J ldquoEye on the Ionosphere The SpatialVariability of Ionospheric Range Delayrdquo GPS Solutions Vol 3 No 32000 pp 70ndash74doi101007PL00012808

[16] Walter T et al ldquoRobust Detection of Ionospheric IrregularitiesrdquoProceedings of IONGPS 2000 httpwaasstanfordedu~wwupapersgpsPDFtoddion00pdf Salt Lake City UT 2000 pp 209ndash218

[17] Christie J R I Ko P-Y Hansen A Dai D Pullen S Pervan Band Parkinson B ldquoThe Effects of Local Ionospheric Decorrelation onLAAS Theory and Experimental Resultsrdquo Proceedings of the Instituteof Navigation National Technical Meeting 1999 pp 769ndash777

[18] ldquoMinimum Operational Performance Standards for GPS Local AreaAugmentation System Airborne Equipmentrdquo Radio TechnicalCommission for Aeronautics Washington DC Publ DO-253C2008

[19] Lee J Pullen S Datta-Barua S and Enge P ldquoAssessment ofNominal Ionosphere Spatial Decorrelation for GPS-Based AircraftLanding Systemsrdquo Journal of Aircraft Vol 44 No 5 2007 pp 1662ndash1669doi102514128199

[20] Datta-Barua SWalter T Pullen S LuoM Blanch J and Enge PldquoUsing WAAS Ionospheric Data to Estimate LAAS Short BaselineGradientsrdquo Proceedings of the Institute of Navigation National

Technical Meeting 2002 pp 523ndash530[21] Luo M Pullen S Akos D Xie G Datta-Barua S Walter T and

Enge P ldquoAssessment of Ionospheric Impact on LAAS Using WAASSupertruth Datardquo Proceedings of the ION 58th Annual Meeting and

CIGTF 21st Guidance Test Symposium 2002 pp 175ndash186[22] Ene A Qiu D Luo M Pullen S and Enge P ldquoAComprehensive

Ionosphere Storm Data Analysis Method to Support LAAS ThreatModel Developmentrdquo Proceedings of the Institute of Navigation

National Technical Meeting 2005 pp 110ndash130[23] Ramakrishnan S Lee J Pullen S andEnge P ldquoTargetedEphemeris

Decorrelation Parameter Inflation for Improved LAAS AvailabilityDuring Severe Ionosphere Anomaliesrdquo Proceedings of the 2008

National TechnicalMeeting of the Institute of Navigation pp 354ndash366[24] Komjathy A L Sparks and A J Mannucci ldquoGenerating High

Precision Ionospheric Ground-Truth Measurementsrdquo US PatentNo 7289061 B2 filed 30 Oct 2007

[25] Datta-Barua S ldquoIonospheric Threats to the Integrity of Airborne GPSUsersrdquo PhD Dissertation Stanford Univ Palo Alto CA 2008

[26] Luo M Pullen S Dennis J Konno H Xie G Walter T Enge PDatta-Barua S and Dehel T ldquoLAAS Ionosphere Spatial GradientThreat Model and Impact of LGF and Airborne MonitoringrdquoProceedings of the 16th International TechnicalMeeting of the Satellite

Division of the Institute of Navigation IONGPSGNSS 2003 pp 2255ndash2274

[27] Luo M Pullen S Walter T and Enge P ldquoIonosphere SpatialGradient Threat for LAAS Mitigation and Tolerable Threat SpacerdquoProceedings of the 2004 National Technical Meeting of the Institute of

Navigation pp 490ndash501[28] National Geodetic Survey CORS Team ldquoCORS Continuously

Operating Reference Stationsrdquo httpwwwngsnoaagovCORS[retrieved 6 Jan 2009]

[29] National Geophysical Data Center ldquoSpace Physics Interactive DataResourcerdquo 2005 httpspidrngdcnoaagovspidr [retrieved1 March 2005]

[30] Sugiura M and Kamei T ldquoEquatorial Dst Index 1957ndash1986rdquoInternational Association of Geomagnetism and Aeronomy Vol 401991 pp 7ndash14

[31] Menvielle M and Berthelier A ldquoThe K-Derived Planetary IndicesDescription and Availabilityrdquo Reviews of Geophysics Vol 29 No 3

1150 DATTA-BARUA ETAL

receiver data identifying anomalously high spatial rates of changevalidating anomalies that are due to ionospheric events andestimating parameters in terms of the threatmodel of Sec III is shownin Fig 6 The starting point is raw L1 and L2 measurements fromselected clusters of CORS stations An automated algorithm flowingdown the left-hand side of the figure involves using the JPL-processed CORS-based estimates of ionospheric delay to computeapparent spatial gradients and front speeds and screen them forobvious receiver or data-processing errors The remaining points arecompared manually (right-hand side of Fig 6) to estimates derivedfrom raw CORS data The following sections trace through themethodology outlined in the flowchart in Fig 6

A Ionospheric Spatial-Gradient Estimation

1 Station Pair Method

Our approach involves considering selected pairs of CORSstations in the JPL-processed database as though they represent anLGF-user receiver pair (with a static user position) This techniquecalled the station pair method is illustrated in Fig 7 For each epocht the delays at each of two stations i and j viewing the same satellite kare differenced The slope of ionospheric delayrI between all pairsof receivers looking at each satellite is estimated by dividing thedifferential slant delay by the baseline distance between the tworeceivers

rIt jIki t Ikj tjkxi xjk

(7)

The station pair method unlike the time-step method (Sec II)does not artificially inflate ionospheric gradients due to relativemotions of IPPs and the ionosphere [19]

The dual-frequency data in each CORS cluster are separated intofive elevation bins 0ndash12 12ndash20 20ndash30 30ndash45 and 45ndash90 deg Foreach LOS elevation range receivers in each cluster are grouped intopairs and IDF is used for I in Eq (7) to give a dual-frequencyestimate of the gradient rIDF

2 Automated Screening

AfterrIDF is computed an automated screening process (bottom-left corner of Fig 6) isolates the most credible events above a chosenthreshold (100 mm=km) and eliminates those gradients that areapparently due to nonionospheric causes Cases for which tworeceivers are effectively collocated (less than 100m apart) or the slantgradient rIDF does not vary in time are attributed to an interreceiverbias and eliminated Cases for which ionospheric delay measure-ments from one receiver do not vary in time are attributed to a faultyreceiver and eliminated

Variations greater than 15 mm=s ionospheric delay rate of changeare flagged for possible ionospheric activity The 15 mm=s thresholdis set to be an order of magnitude lower than the largest time rate ofchange observed for the 29 Oct 2003 storm day [35] in order toallow significant rapid changes in the delay value to pass screening aspossible ionospheric events The advantage of this threshold is thatreal ionospheric events are less likely to be eliminated as falsenegatives The tradeoff is that false alarms occur when the datacontain cycle slips causing the delay value to jump suddenlybetween two successive measurement points These cases have to beeliminated manually from the screening results

3 Manual Validation

The plausible ionospheric events that survive the computer-automated screening process are analyzed manually For each ofthese events visual analysis determineswhether themagnitude of thegradients together with the length of time that these gradientsmanifest themselves look reasonable or aremore likely to be artifactsof faulty receiver tracking

The first step in verifying an event is by comparison againstsimultaneous observations of slant delay from other nearby receiversin the same cluster This can be thought of as cluster-wide voting thatoperates in a similar manner to the voting among colocated receiversat each WAAS reference station If other nearby CORS receiversexhibit similar ionospheric delay patterns to the pair that shows themost-extreme spatial decorrelation the event is very probably realand is not limited to a single receiver fault On the other hand if theobservations were influenced by receiver artifacts in the data it isunlikely that amajority of the receivers in the cluster contain the sameor similar artifacts

To support this comparison plots are constructed that extendacross the entire period of time for which data was extracted so thatthe receiver behavior can be analyzed both in the presence ofdisturbed ionosphere and also during calm ionospheric conditions oneither side of the disturbance While the electron content within theionosphere is expected to vary wildly during the actual storm delaymeasurements from the different receivers within a CORS cluster areexpected to converge to similar values at times of nominalionospheric activity Quiet periods of ionospheric behavior help toestablish a baseline for the affected receiver cluster and give anindication of any possible interreceiver biases

The next step in validating extreme gradients (top-right corner ofFig 6) is to estimate approximate wave-front parameters from rawL1 code-minus-carrier measurements ICMC and compare them to theobservations from the JPL-processed dual-frequency data IDF TheL1-only estimates of these slopesrICMC given by using ICMC for theslant delay I in Eq (7) provide a picture of gradients that is notsubject to fragile L2 tracking loops The L1-only estimates are notexact however because the integer ambiguity N1 has not beeneliminated

Two methods are used to remove the integer ambiguity for high-elevation and low-elevation LOSs respectively For high elevationthe postprocessed L1L2 estimate of absolute differential sloperIDFat a timewell before or after thewave front passes is used to initializethe single-frequency ionospheric gradient estimates rICMC A pointin time is picked at which the measurements rICMC are aligned torIDF by subtracting a constant bias from all the measurement of oneof the receivers equalizing the value that they measure at the choseninstant Since the differential delay between a pair of receivers isobserved this is equivalent to choosing a reference zero-slope point

L1 code and carrier

L1 code and carrier L2 carrier

ldquoRawrdquo IGSCORS Data

JPL Ionosphere ldquoTruthrdquo Processing

Ionosphere Delay Estimates

Find Maximum Apparent Gradients

(Station Pair Method)

Screening Process (Automated)

Database of Extreme Gradients Erroneous Receiver

Steps and L1L2 Biases Removed Investigate

Remaining Points

Remove ldquoQuestionablerdquoObservations

Estimate Gradients from L1 code-minus-carrier

Output Database of Maximum Gradients

Compare L1L2 and L1 CMC Gradient Obs

ldquoValidatedrdquoMax Gradient

Database

Fig 6 Ionospheric anomaly data analysis procedure

Fig 7 Station pair method Not to scale

1146 DATTA-BARUA ETAL

in rICMC at a particular instant in time and then expressing all otherslope measurements relative to it Thus L1 CMC ionosphericgradient estimates form lower bounds on the true gradient but theseestimates are accurate enough that they can be used to validate theL1L2 observations

For low-elevation satellites the track may not be long enough tosample through quiet ionosphere In this case the single-frequencygradient estimate is leveled to the mean of the JPL-processed dual-frequency estimate rIDF as shown in Eq (8)

r ~ICMCt rICMCt rICMC rIDF (8)

Anomalous ionospheric events not eliminated by this final manual

comparison to single-frequency estimates r ~ICMC of the spatialvariation are deemed to be validated measurements rIDFValidated andare used to generate bounds on the threat model parameter g

gmaxtrIDFValidated (9)

B Ionospheric Front Speed Estimation

Observations of severe gradients that are validated as described inSec VA are studied inmore detail to characterize them in the contextof the ionospheric threatmodel geometry The automated code used athree-station-based trigonometric algorithm to estimate the speedand direction of wave-front propagation with respect to the ground(see [22] for details) The travel time of the frontwhich swept a pair ofstations is measured to compute the speed and the third station isused to observe the front direction This algorithm proved to be verysensitive to measurement errors and limitations of the threat model(in particular the assumption that the wave front is a straight line)when extrapolated to cover three (instead of two) stations

To overcome the weakness of the automated ionospheric frontspeed computation method a manual two-station-based method isused that takes wave-front orientation (estimated manually based onmany stations) as an input so that only speed needs to be solved forFirst we form groups that typically include four to eight nearbyCORS receiverswhich show similar ionospheric delay patterns to thesatellite-receiver pair from which anomalous gradients wereobserved and verified By observing the time history of ionosphericslant delays from the stations observing the same satellite the peakdelay times tpeaki at which the slant delay of the station i reached thelocal maximum value are determined Because ionospheric delays ofthese nearby stations exhibit similar patterns the order in which thestations were affected by the ionospheric front can be deduced fromtpeaki This order and the locations of the affected stations make itpossible to approximately determine the forward propagationdirection of the front Since it was assumed that the wave front is astraight line and moves with constant speed which is not true inreality the sequence and tpeaki must be carefully adjusted manuallyAlso the slant delays sometimes appear to have more than one localmaximum and this ambiguity makes room for tpeaki to be fine-tunedconsidering the underlying assumptions

Next we select a pair of stations i j and determine the traveltime tij given by Eq (10)

tijinej tpeaki tpeakj i j 1 2 N (10)

The distance between the two stations projected onto the lineperpendicular to the wave frontdn is used to compute the speed ofthe wave front vn as shown in Eq (11)

vn dn

tijinej(11)

Note that this speed estimate contains the velocity componentresulting from the movement of the satellite in addition to that fromactual wave-front motion Thus the (notional) IPP velocity iscomputed and removed so that the resulting wave-front velocityestimates are referenced to a fixed point on the ground (such as anLGF site) IPP velocity and direction are computed from the CORS

data which give the notional IPP locations for each satellite at 30 sintervals The purpose of using IPP velocity even though the IPPconcept is not otherwise used in defining this threat model is tocapture in an average sense the speed of the ray-path motion throughthe ionosphere We repeat this computation for all combinations ofstations and obtain a range of front-speed estimates that are normal tothe ionospheric front Finally we take themean value of the resultingspeed estimates which is the manually estimated and validatedwave-front velocity with respect to the ground

VI Results CONUS Ionospheric AnomalyThreat Model

In this section we analyze two case studies following the processdescribed in Sec V These two cases are of particular interest becausethey give the maximum dual-frequency verified slopes we observedat high elevation (Sec VIA) and at low elevation (Sec VIB)respectively Both cases were observed from the OhioMichigancluster shown in the inset of Fig 3 and show very weak elevationdependence We then summarize the resulting threat model forCONUS

A Case Study 1 High Elevation

Figure 8 shows the slant ionospheric delay from this event asobserved by seven CORS reference stations in northern Ohio andsouthern Michigan as they tracked GPS SVN 38 Note the rapidgrowth in delay associatedwith the passage of the leading edge of theplume of enhanced delay shown in Fig 2 in just under an hourfollowed by an interval of erratic variation in ionospheric delaywithin the plumewhile the overall delay remains high followed by asudden steep drop-off corresponding to the duskward edge of theplume The JPL-processed dual-frequency delays (Fig 9a) andelevation angles (Fig 9c) over time are shown for ZOB1 (red) andGARF (green) which are separated by only 512 km The dual-frequency estimate of the sloperIDF between these stations is shownin Fig 9b

Data outages on both ZOB1rsquos and GARFrsquos dual-frequencymeasurements are visible in Fig 9a The drop from 30 to 18 m on theGARF (green) curve at 2055UTand on ZOB1 (red) at 2100 UT callsinto question the reliability of the dual-frequency estimate of theslope For this reason we validate the presence of an actual iono-spheric spatial anomaly within this data by comparing rIDF (blue)with the single-frequency estimate of the slope rICMC (red) inFig 10 The data outages present from 2055 to 2100 UT in rIDF donot exist in the L1-only measurements confirming that the data gapsare limited to L2measurements The general trend over time betweenthe two slope measurements agrees including the time during whichL2 data outages occur Based on the similarity of this event toobservations (Fig 8) by other receivers in the vicinity we can

Fig 8 Dual-frequency estimates of slant ionospheric delay from JPL-

processed CORS data vs UT hour for select stations in the Ohio

Michigan cluster tracking SVN 38 at high elevation

DATTA-BARUA ETAL 1147

confirm amaximum slope observed due to this event of 413 mm=kmat 2100 UT

B Case Study 2 Low Elevation

We also investigate data to show the largest-magnitude ion-ospheric gradients that we have validated at low elevation (less than15 deg) at 2130 UT Figure 11 is a plot of the dual-frequencymeasurements of slant ionospheric delay over time at these stationswhen observing SVN 26 to the northwest at about 12 deg elevation(not shown) The two stations FREO and WOOS (see inset of Fig 3for location) begin at 15 m and rise to over 45 m of slant delay as the

LOSs from these stations to SVN 26 pass through the filament ofenhanced ionospheric delay

We analyze the WOOSndashGARF pair of stations to estimate theionospheric spatial gradient between them The dual-frequency-based slope rIDF is plotted in blue on Fig 12 The slope rises from0 mm=km to nearly 400 mm=km as the delay for WOOS rises to45 m during passage through the anomalous region while GARFholds steady at about 15ndash18m There is a dual-frequency data gap forGARF at 2054 UT that contributes to the data gap inrIDF howeverthe outage is not simultaneous with the highest slope observedbetween this station pair

The dual-frequency estimate is compared with the single-frequency estimate of the ionospheric spatial gradientrICMC (greenon Fig 12) Continuous single-frequencymeasurements exist exceptat one epoch at 2052 UT at which time the L1 measurements had avisible cycle slip that was manually removed Based on theagreement between the two measurements we conclude that thehighest slope between the stationsWOOS and GARF occurs at 2120UT and is about 360 mm=km

C LAAS Ionospheric Threat Space

Using the method described in Sec V and illustrated in thesubsections above numerous other instances of high differentialdelay between receivers closer than 100 km were investigated frommultiple storm days in CONUS listed in Table 1 This was amultistage multiperson process spanning data from storms from

20 202 204 206 208 21 212 214 216 218 220

10

20

30

40Dual-frequencyZOB1 GARF SV38

Sla

nt

del

ay (

m)

ZOB1

GARF

20 202 204 206 208 21 212 214 216 218 22-200

0

200

400

600

Ion

o s

lop

e (m

mk

m)

20 202 204 206 208 21 212 214 216 218 2240

50

60

70

80

Ele

vati

on

(d

eg)

Time (hour of 11202003)

a)

b)

c)

Fig 9 Maximum ionospheric gradient observed at high elevation

a) dual-frequency estimates of slant ionospheric delay b) spatial rate of

change and c) elevation from JPL-processed CORS data vs UT hour for

receivers ZOB1 and GARF tracking SVN 38 at high elevation

20 202 204 206 208 21 212 214 216 218 22-50

0

50

100

150

200

250

300

350

400

450

Time (hour of 11202003)

Ion

o S

lop

e (m

mk

m)

ZOB1 and GARF Slopes Comparison SV38

DF Slope

L1 Slope

Fig 10 Spatial rate of change of ionospheric delay over time between

two stations in OhioMichigan cluster viewing SVN 38 at high elevation

from rIDF (blue) and rICMC (red)

207 208 209 21 211 212 213 2140

5

10

15

20

25

30

35

40

45

50Iono Delay for Selected Stations SV 26 Elevation100689ordm - 12078ordm

Time(Hour of 11202003)

Sla

nt Io

no D

elay

(m

)

FREO

WOOS

LSBN

GARFGUST

Fig 11 Dual-frequency estimates of slant ionospheric delay from JPL-processed CORS data vs UT hour for select stations in the Ohio

Michigan cluster tracking SVN 26 at low elevation

208 209 21 211 212 213 214-50

0

50

100

150

200

250

300

350

400

Time (hour of 11202003)

Ion

o S

lop

e (m

mk

m)

WOOS and GARF Slopes Comparison SV26

DF Slope

L1 Slope

Fig 12 Spatial rate of change of ionospheric delay over time between

two stations inOhioMichigan cluster viewing SVN26at lowelevation as

from rIDF (blue) and rICMC (green)

1148 DATTA-BARUA ETAL

2000ndash2005 during the maximum and waning phases of the mostrecent solar cycle

Figure 13 summarizes the results of this data analysis process bypopulating the threat space with cases observed in CORS data fromthe storms examined In Fig 13a the instances of anomalousgradients are plotted as a function of slope (mm=km) on thehorizontal axis and ground speed (m=s) on the vertical axis Thepoints that were identified in dual-frequency data and subsequentlyverified with single-frequency L1 code-minus-carrier data aremarked with diamonds The points that were estimated from therICMC directly are shown as triangles These same points are plottedin Fig 13b as a function of elevation angle (degrees) on the horizontalaxis and slope (mm=km) on the vertical axis

Note that width estimates do not appear in this figure Thewidth ofthe TEC rampwas generally difficult to assess from the available datagiven the typical 50ndash100 km separation of CORS reference stationsIn addition the integrity results (ie the threat model impacts onLAAS users) were not found to be sensitive to this parameter [27]

Empirical bounds are fitted to the points shown in Fig 13 in orderto limit the threat space to the maximum gradients discovered andvalidated These bounds are marked with solid lines in Fig 13Figure 13a shows that for events moving at speeds less than 90 mswith respect to the ground a maximum slope of 150 mmkm boundsthe events observed Figure 13b shows the upper bound on themaximum gradient of this threat model as a function of satelliteelevation angle for speeds greater than 90 ms a slope of375 mm=km at low elevation increasing linearly at a rate of 1 mmkm from 15 to 65 deg elevation to reach 425 mm=km at highelevation For ground speeds below 90 ms the maximum slope is150mmkm independent of elevation These bounds slightly exceedthe largest gradients validated from the data analysis due to marginadded to account for measurement error In addition to the plottedmaximum gradient and speed bounds width (distance between highand low delay regions) is delimited to be between 25 and 200 km andtotal slant differential delay up to 50 m [2236]

Figure 13a shows a maximum estimated ionospheric front speedof about 600 m=s with respect to the ground and the speed was

limited to be nomore than 750 m=s This bound is based on a numberof considerations First in practice the absolute speed is of secondaryimportance to the front speedwith respect to the aircraft andLGF ray-paths (or effectively their IPPs) The relative motion permitsdetectability in fact the estimates of speed were very sensitive to thisrelative motion There is a real (albeit unlikely) possibility that IPPspeed might cancel out the actual ionospheric front speed relative tothe ground and thus prevent time-based LGF monitoring fromdetecting very large gradients for several minutes This possibilitysignificantly increases the maximum possible ionosphere-inducederror for LAAS users beyond what was initially projected in workthat did not consider this masking possibility (eg [37]) It alsoaffects the ionospheric anomaly simulation algorithms described in[2338] as the method for selecting the worst possible ionosphericfront from the point of view of a given satellite is now dictated by thetheoretical speed of the resulting IPP

The threat space shown in Fig 13 is used to represent whatthe ionosphere is potentially capable of during rare extreme-ionospheric-storm conditions based on the validated events observedin the ionospheric data analysis described in Sec V However inadopting this threat model for the present wemust recognize that thevalidated events that have occurred to date are few in number sincethey are driven by the most extreme storms that have impacted theAmerican sector and cover a limited geographical area withinCONUS due to constraints imposed by the CORS receiver sites andequipage It is possible that localized events severe enough topotentially threaten LAAS users have occurred in CONUS but arenot included in the data used to construct the threat space Thereforewhile this threat space is deemed final for the purposes of CAT ILAAS certification [39] the anomaly mitigation algorithms haveadjustable parameters in case future ionospheric storms or dataanalyses reveal gradients that fall outside this threat space Anotherpossibility is that better understanding of how the ionospherebehaves during storm events will allow us to reduce the conservatismin the model as well as develop a more physically accuratedescription of how ionospheric anomalies propagate through LGFand user observations

Users can be protected from these situations by inflating theprotection bound that is broadcast to exclude geometries that wouldlead to intolerably large positioning errors [2338] Even thoughthere is no evidence of such large gradients on nominal days withoutthe ability to detect ionospheric storms in real time that is possessedby WAAS LAAS must assume that the ionosphere may be in adisturbed state at all times As a result geometries that would beunsafe during a storm are disallowed even though severe storms arevery unlikely to be present thus sacrificing precision approachavailability One method for excluding geometries that achieves anacceptable level of availability is described in [23]

VII Conclusions

This paper represents the culmination of several years ofteamwork in conducting as comprehensive a search as possible forpast extreme ionospheric events inCONUS thatwould impact LAASuser safety We have identified the ionospheric threat to users ofLAAS parameterized a model to represent it and applied a two-phase method of ionospheric spatial-gradient analysis and validationto quantify the magnitude of these threats This method consists ofautomatic processing of dual-frequency GPS carrier-phase measure-ments of ionospheric delay combined with manual comparison tosingle-frequency code-carrier divergence measurements to searchfor an upper bound on possible anomalous ionospheric gradientsThe final threat space of the model is ultimately driven by the largestof these points found but the parameters of this model have gonethrough a number of revisions in magnitude throughout this processThese values represent our best-validated observations during whatare recognized within the ionospheric community as the mostextreme geomagnetic storms during and following the most recentsolar maximum

The most time-consuming aspect of ionospheric anomaly dataanalysis is manual review of the apparent anomalies output by the

Fig 13 Ionospheric gradient threat space anomalous ionospheric

gradients observed plotted in threat model space as a function of a) slope

and ground speed and b) elevation and slope

DATTA-BARUA ETAL 1149

automated-search software In themajority of cases it was evident tohuman eyes that receiver or postprocessing errors created an apparentionospheric gradient that was not real and these observations wereset aside However a significant number of events that could not bediscounted remained andwere subjected tomore detailed analysis byteam consensus

As a result of this work we have validated low-elevation spatialgradients in the ionospheric delay at L1 as high as 360 and413 mm=km at high elevation These observations were made withCORS network stations in northern Ohio during the 20 Nov 2003ionospheric storm Station redundancy rules out the possibility ofthese gradients being generated by faulty receivers or significanterrors in receiver bias estimation Simultaneous observations onother satellites at similar elevations were also unusually highTherefore we conclude that the largest gradients observed herewerenot due to nonionospheric causes

The impact of these gradients on WAAS precision approachintegrity is negligible due to the implementation of the WAASextreme storm detector (however WAAS precision approachavailability is dramatically affected when severe storms occur)Mitigating the safety threats posed by ionospheric storms to users ofLAAS is achieved by disallowing specific user satellite geometriesduring precision approach at the cost of somewhat reducing LAASCAT 1 availability

Acknowledgments

The authors thank the FAA LAAS Program Office for theirsupport in the completion of this effort We would also like to thankspecific individuals who helped us in this work including ToddWalter Juan Blanch and Shankar Ramakrishnan at Stanford JohnWarburton and Tom Dehel of the Federal Aviation AdministrationWilliam J Hughes Technical Center and Attila Komjathy of theNASA Jet Propulsion Laboratory

References

[1] Enge P ldquoLocal AreaAugmentation ofGPS for the PrecisionApproachof Aircraftrdquo Proceedings of the IEEE Vol 87 No 1 Jan 1999pp 111ndash132doi1011095736345

[2] Enge P Walter T Pullen S Kee C Chao Y C and Tsai Y JldquoWide Area Augmentation of the Global Positioning SystemrdquoProceedings of the IEEE Vol 84 No 8 Aug 1996 pp 1063ndash1088doi1011095533954

[3] Misra P and Enge P Global Positioning System Signals

Measurement and Performance 2nd ed GangandashJamuna LincolnMA 2006

[4] Tascione T F Introduction to the Space Environment KriegerMalabar FL 1994

[5] Klobuchar J ldquoIonospheric Effects on GPSrdquo Global Positioning

System Theory and Applications edited by B W Parkinson and J JSpilker Vol 1 Progress in Astronautics and Aeronautics AIAAWashington DC 1996 pp 485ndash515

[6] Datta-Barua S Mannucci A J Walter T and Enge P ldquoAltitudinalVariation of Midlatitude Localized TEC Enhancement from Ground-and Space-BasedMeasurementsrdquo Space Weather Vol 6 No S10D062008doi1010292008SW000396

[7] Roble R G and Ridley E C ldquoThermospherendashIonospherendashMesosphere Electrodynamics General Circulation Model (TIME-GCM) Equinox Solar Cycle Minimum Simulations (30ndash500 km)rdquoGeophysical Research Letters Vol 21 No 6 1994 pp 417ndash420doi10102993GL03391

[8] Crowley G et al ldquoGlobal ThermospherendashIonosphere Response toOnset of 20 November 2003Magnetic Stormrdquo Journal of GeophysicalResearch Vol 111 No A10S18 2006doi1010292005JA011518

[9] Huba J D Joyce G and Fedder J A ldquoSami2 is Another Model ofthe Ionosphere (SAMI2) A New Low-Latitude Ionosphere ModelrdquoJournal of Geophysical Research Vol 105 No A10 2000 pp 23035ndash23053doi1010292000JA000035

[10] Bilitza D ldquoInternational Reference Ionosphere 2000rdquo Radio ScienceVol 36 No 2 MarchndashApril 2001 pp 261ndash275

doi1010292000RS002432[11] PiXWangC Hajj GA RosenGWilson BD andBaileyG J

ldquoEstimation of ExB Drift Using a Global Assimilative IonosphericModel An Observation System Simulation Experimentrdquo Journal of

Geophysical Research Vol 108(A2) No 1075 2003doi1010292001JA009235

[12] Schunk R et al ldquoGlobal Assimilation of Ionospheric Measurements(GAIM)rdquo Radio Science Vol 39 No RS1S02 2004doi1010292002RS002794

[13] Bust G S et al ldquoFour-Dimensional GPS Imaging of Space WeatherStormsrdquo Space Weather Vol 5 No S02003 2007doi1010292006SW000237

[14] Klobuchar J A Doherty P H and El-Arini M B ldquoPotentialIonospheric Limitations to Wide-Area Differential GPSrdquo Proceedingsof the 16th International Technical Meeting of the Satellite Division of

The Institute Of Navigation (IONGPS 1993) Salt Lake City UT 1993pp 1245ndash1254

[15] Klobuchar J A and Kunches J ldquoEye on the Ionosphere The SpatialVariability of Ionospheric Range Delayrdquo GPS Solutions Vol 3 No 32000 pp 70ndash74doi101007PL00012808

[16] Walter T et al ldquoRobust Detection of Ionospheric IrregularitiesrdquoProceedings of IONGPS 2000 httpwaasstanfordedu~wwupapersgpsPDFtoddion00pdf Salt Lake City UT 2000 pp 209ndash218

[17] Christie J R I Ko P-Y Hansen A Dai D Pullen S Pervan Band Parkinson B ldquoThe Effects of Local Ionospheric Decorrelation onLAAS Theory and Experimental Resultsrdquo Proceedings of the Instituteof Navigation National Technical Meeting 1999 pp 769ndash777

[18] ldquoMinimum Operational Performance Standards for GPS Local AreaAugmentation System Airborne Equipmentrdquo Radio TechnicalCommission for Aeronautics Washington DC Publ DO-253C2008

[19] Lee J Pullen S Datta-Barua S and Enge P ldquoAssessment ofNominal Ionosphere Spatial Decorrelation for GPS-Based AircraftLanding Systemsrdquo Journal of Aircraft Vol 44 No 5 2007 pp 1662ndash1669doi102514128199

[20] Datta-Barua SWalter T Pullen S LuoM Blanch J and Enge PldquoUsing WAAS Ionospheric Data to Estimate LAAS Short BaselineGradientsrdquo Proceedings of the Institute of Navigation National

Technical Meeting 2002 pp 523ndash530[21] Luo M Pullen S Akos D Xie G Datta-Barua S Walter T and

Enge P ldquoAssessment of Ionospheric Impact on LAAS Using WAASSupertruth Datardquo Proceedings of the ION 58th Annual Meeting and

CIGTF 21st Guidance Test Symposium 2002 pp 175ndash186[22] Ene A Qiu D Luo M Pullen S and Enge P ldquoAComprehensive

Ionosphere Storm Data Analysis Method to Support LAAS ThreatModel Developmentrdquo Proceedings of the Institute of Navigation

National Technical Meeting 2005 pp 110ndash130[23] Ramakrishnan S Lee J Pullen S andEnge P ldquoTargetedEphemeris

Decorrelation Parameter Inflation for Improved LAAS AvailabilityDuring Severe Ionosphere Anomaliesrdquo Proceedings of the 2008

National TechnicalMeeting of the Institute of Navigation pp 354ndash366[24] Komjathy A L Sparks and A J Mannucci ldquoGenerating High

Precision Ionospheric Ground-Truth Measurementsrdquo US PatentNo 7289061 B2 filed 30 Oct 2007

[25] Datta-Barua S ldquoIonospheric Threats to the Integrity of Airborne GPSUsersrdquo PhD Dissertation Stanford Univ Palo Alto CA 2008

[26] Luo M Pullen S Dennis J Konno H Xie G Walter T Enge PDatta-Barua S and Dehel T ldquoLAAS Ionosphere Spatial GradientThreat Model and Impact of LGF and Airborne MonitoringrdquoProceedings of the 16th International TechnicalMeeting of the Satellite

Division of the Institute of Navigation IONGPSGNSS 2003 pp 2255ndash2274

[27] Luo M Pullen S Walter T and Enge P ldquoIonosphere SpatialGradient Threat for LAAS Mitigation and Tolerable Threat SpacerdquoProceedings of the 2004 National Technical Meeting of the Institute of

Navigation pp 490ndash501[28] National Geodetic Survey CORS Team ldquoCORS Continuously

Operating Reference Stationsrdquo httpwwwngsnoaagovCORS[retrieved 6 Jan 2009]

[29] National Geophysical Data Center ldquoSpace Physics Interactive DataResourcerdquo 2005 httpspidrngdcnoaagovspidr [retrieved1 March 2005]

[30] Sugiura M and Kamei T ldquoEquatorial Dst Index 1957ndash1986rdquoInternational Association of Geomagnetism and Aeronomy Vol 401991 pp 7ndash14

[31] Menvielle M and Berthelier A ldquoThe K-Derived Planetary IndicesDescription and Availabilityrdquo Reviews of Geophysics Vol 29 No 3

1150 DATTA-BARUA ETAL

in rICMC at a particular instant in time and then expressing all otherslope measurements relative to it Thus L1 CMC ionosphericgradient estimates form lower bounds on the true gradient but theseestimates are accurate enough that they can be used to validate theL1L2 observations

For low-elevation satellites the track may not be long enough tosample through quiet ionosphere In this case the single-frequencygradient estimate is leveled to the mean of the JPL-processed dual-frequency estimate rIDF as shown in Eq (8)

r ~ICMCt rICMCt rICMC rIDF (8)

Anomalous ionospheric events not eliminated by this final manual

comparison to single-frequency estimates r ~ICMC of the spatialvariation are deemed to be validated measurements rIDFValidated andare used to generate bounds on the threat model parameter g

gmaxtrIDFValidated (9)

B Ionospheric Front Speed Estimation

Observations of severe gradients that are validated as described inSec VA are studied inmore detail to characterize them in the contextof the ionospheric threatmodel geometry The automated code used athree-station-based trigonometric algorithm to estimate the speedand direction of wave-front propagation with respect to the ground(see [22] for details) The travel time of the frontwhich swept a pair ofstations is measured to compute the speed and the third station isused to observe the front direction This algorithm proved to be verysensitive to measurement errors and limitations of the threat model(in particular the assumption that the wave front is a straight line)when extrapolated to cover three (instead of two) stations

To overcome the weakness of the automated ionospheric frontspeed computation method a manual two-station-based method isused that takes wave-front orientation (estimated manually based onmany stations) as an input so that only speed needs to be solved forFirst we form groups that typically include four to eight nearbyCORS receiverswhich show similar ionospheric delay patterns to thesatellite-receiver pair from which anomalous gradients wereobserved and verified By observing the time history of ionosphericslant delays from the stations observing the same satellite the peakdelay times tpeaki at which the slant delay of the station i reached thelocal maximum value are determined Because ionospheric delays ofthese nearby stations exhibit similar patterns the order in which thestations were affected by the ionospheric front can be deduced fromtpeaki This order and the locations of the affected stations make itpossible to approximately determine the forward propagationdirection of the front Since it was assumed that the wave front is astraight line and moves with constant speed which is not true inreality the sequence and tpeaki must be carefully adjusted manuallyAlso the slant delays sometimes appear to have more than one localmaximum and this ambiguity makes room for tpeaki to be fine-tunedconsidering the underlying assumptions

Next we select a pair of stations i j and determine the traveltime tij given by Eq (10)

tijinej tpeaki tpeakj i j 1 2 N (10)

The distance between the two stations projected onto the lineperpendicular to the wave frontdn is used to compute the speed ofthe wave front vn as shown in Eq (11)

vn dn

tijinej(11)

Note that this speed estimate contains the velocity componentresulting from the movement of the satellite in addition to that fromactual wave-front motion Thus the (notional) IPP velocity iscomputed and removed so that the resulting wave-front velocityestimates are referenced to a fixed point on the ground (such as anLGF site) IPP velocity and direction are computed from the CORS

data which give the notional IPP locations for each satellite at 30 sintervals The purpose of using IPP velocity even though the IPPconcept is not otherwise used in defining this threat model is tocapture in an average sense the speed of the ray-path motion throughthe ionosphere We repeat this computation for all combinations ofstations and obtain a range of front-speed estimates that are normal tothe ionospheric front Finally we take themean value of the resultingspeed estimates which is the manually estimated and validatedwave-front velocity with respect to the ground

VI Results CONUS Ionospheric AnomalyThreat Model

In this section we analyze two case studies following the processdescribed in Sec V These two cases are of particular interest becausethey give the maximum dual-frequency verified slopes we observedat high elevation (Sec VIA) and at low elevation (Sec VIB)respectively Both cases were observed from the OhioMichigancluster shown in the inset of Fig 3 and show very weak elevationdependence We then summarize the resulting threat model forCONUS

A Case Study 1 High Elevation

Figure 8 shows the slant ionospheric delay from this event asobserved by seven CORS reference stations in northern Ohio andsouthern Michigan as they tracked GPS SVN 38 Note the rapidgrowth in delay associatedwith the passage of the leading edge of theplume of enhanced delay shown in Fig 2 in just under an hourfollowed by an interval of erratic variation in ionospheric delaywithin the plumewhile the overall delay remains high followed by asudden steep drop-off corresponding to the duskward edge of theplume The JPL-processed dual-frequency delays (Fig 9a) andelevation angles (Fig 9c) over time are shown for ZOB1 (red) andGARF (green) which are separated by only 512 km The dual-frequency estimate of the sloperIDF between these stations is shownin Fig 9b

Data outages on both ZOB1rsquos and GARFrsquos dual-frequencymeasurements are visible in Fig 9a The drop from 30 to 18 m on theGARF (green) curve at 2055UTand on ZOB1 (red) at 2100 UT callsinto question the reliability of the dual-frequency estimate of theslope For this reason we validate the presence of an actual iono-spheric spatial anomaly within this data by comparing rIDF (blue)with the single-frequency estimate of the slope rICMC (red) inFig 10 The data outages present from 2055 to 2100 UT in rIDF donot exist in the L1-only measurements confirming that the data gapsare limited to L2measurements The general trend over time betweenthe two slope measurements agrees including the time during whichL2 data outages occur Based on the similarity of this event toobservations (Fig 8) by other receivers in the vicinity we can

Fig 8 Dual-frequency estimates of slant ionospheric delay from JPL-

processed CORS data vs UT hour for select stations in the Ohio

Michigan cluster tracking SVN 38 at high elevation

DATTA-BARUA ETAL 1147

confirm amaximum slope observed due to this event of 413 mm=kmat 2100 UT

B Case Study 2 Low Elevation

We also investigate data to show the largest-magnitude ion-ospheric gradients that we have validated at low elevation (less than15 deg) at 2130 UT Figure 11 is a plot of the dual-frequencymeasurements of slant ionospheric delay over time at these stationswhen observing SVN 26 to the northwest at about 12 deg elevation(not shown) The two stations FREO and WOOS (see inset of Fig 3for location) begin at 15 m and rise to over 45 m of slant delay as the

LOSs from these stations to SVN 26 pass through the filament ofenhanced ionospheric delay

We analyze the WOOSndashGARF pair of stations to estimate theionospheric spatial gradient between them The dual-frequency-based slope rIDF is plotted in blue on Fig 12 The slope rises from0 mm=km to nearly 400 mm=km as the delay for WOOS rises to45 m during passage through the anomalous region while GARFholds steady at about 15ndash18m There is a dual-frequency data gap forGARF at 2054 UT that contributes to the data gap inrIDF howeverthe outage is not simultaneous with the highest slope observedbetween this station pair

The dual-frequency estimate is compared with the single-frequency estimate of the ionospheric spatial gradientrICMC (greenon Fig 12) Continuous single-frequencymeasurements exist exceptat one epoch at 2052 UT at which time the L1 measurements had avisible cycle slip that was manually removed Based on theagreement between the two measurements we conclude that thehighest slope between the stationsWOOS and GARF occurs at 2120UT and is about 360 mm=km

C LAAS Ionospheric Threat Space

Using the method described in Sec V and illustrated in thesubsections above numerous other instances of high differentialdelay between receivers closer than 100 km were investigated frommultiple storm days in CONUS listed in Table 1 This was amultistage multiperson process spanning data from storms from

20 202 204 206 208 21 212 214 216 218 220

10

20

30

40Dual-frequencyZOB1 GARF SV38

Sla

nt

del

ay (

m)

ZOB1

GARF

20 202 204 206 208 21 212 214 216 218 22-200

0

200

400

600

Ion

o s

lop

e (m

mk

m)

20 202 204 206 208 21 212 214 216 218 2240

50

60

70

80

Ele

vati

on

(d

eg)

Time (hour of 11202003)

a)

b)

c)

Fig 9 Maximum ionospheric gradient observed at high elevation

a) dual-frequency estimates of slant ionospheric delay b) spatial rate of

change and c) elevation from JPL-processed CORS data vs UT hour for

receivers ZOB1 and GARF tracking SVN 38 at high elevation

20 202 204 206 208 21 212 214 216 218 22-50

0

50

100

150

200

250

300

350

400

450

Time (hour of 11202003)

Ion

o S

lop

e (m

mk

m)

ZOB1 and GARF Slopes Comparison SV38

DF Slope

L1 Slope

Fig 10 Spatial rate of change of ionospheric delay over time between

two stations in OhioMichigan cluster viewing SVN 38 at high elevation

from rIDF (blue) and rICMC (red)

207 208 209 21 211 212 213 2140

5

10

15

20

25

30

35

40

45

50Iono Delay for Selected Stations SV 26 Elevation100689ordm - 12078ordm

Time(Hour of 11202003)

Sla

nt Io

no D

elay

(m

)

FREO

WOOS

LSBN

GARFGUST

Fig 11 Dual-frequency estimates of slant ionospheric delay from JPL-processed CORS data vs UT hour for select stations in the Ohio

Michigan cluster tracking SVN 26 at low elevation

208 209 21 211 212 213 214-50

0

50

100

150

200

250

300

350

400

Time (hour of 11202003)

Ion

o S

lop

e (m

mk

m)

WOOS and GARF Slopes Comparison SV26

DF Slope

L1 Slope

Fig 12 Spatial rate of change of ionospheric delay over time between

two stations inOhioMichigan cluster viewing SVN26at lowelevation as

from rIDF (blue) and rICMC (green)

1148 DATTA-BARUA ETAL

2000ndash2005 during the maximum and waning phases of the mostrecent solar cycle

Figure 13 summarizes the results of this data analysis process bypopulating the threat space with cases observed in CORS data fromthe storms examined In Fig 13a the instances of anomalousgradients are plotted as a function of slope (mm=km) on thehorizontal axis and ground speed (m=s) on the vertical axis Thepoints that were identified in dual-frequency data and subsequentlyverified with single-frequency L1 code-minus-carrier data aremarked with diamonds The points that were estimated from therICMC directly are shown as triangles These same points are plottedin Fig 13b as a function of elevation angle (degrees) on the horizontalaxis and slope (mm=km) on the vertical axis

Note that width estimates do not appear in this figure Thewidth ofthe TEC rampwas generally difficult to assess from the available datagiven the typical 50ndash100 km separation of CORS reference stationsIn addition the integrity results (ie the threat model impacts onLAAS users) were not found to be sensitive to this parameter [27]

Empirical bounds are fitted to the points shown in Fig 13 in orderto limit the threat space to the maximum gradients discovered andvalidated These bounds are marked with solid lines in Fig 13Figure 13a shows that for events moving at speeds less than 90 mswith respect to the ground a maximum slope of 150 mmkm boundsthe events observed Figure 13b shows the upper bound on themaximum gradient of this threat model as a function of satelliteelevation angle for speeds greater than 90 ms a slope of375 mm=km at low elevation increasing linearly at a rate of 1 mmkm from 15 to 65 deg elevation to reach 425 mm=km at highelevation For ground speeds below 90 ms the maximum slope is150mmkm independent of elevation These bounds slightly exceedthe largest gradients validated from the data analysis due to marginadded to account for measurement error In addition to the plottedmaximum gradient and speed bounds width (distance between highand low delay regions) is delimited to be between 25 and 200 km andtotal slant differential delay up to 50 m [2236]

Figure 13a shows a maximum estimated ionospheric front speedof about 600 m=s with respect to the ground and the speed was

limited to be nomore than 750 m=s This bound is based on a numberof considerations First in practice the absolute speed is of secondaryimportance to the front speedwith respect to the aircraft andLGF ray-paths (or effectively their IPPs) The relative motion permitsdetectability in fact the estimates of speed were very sensitive to thisrelative motion There is a real (albeit unlikely) possibility that IPPspeed might cancel out the actual ionospheric front speed relative tothe ground and thus prevent time-based LGF monitoring fromdetecting very large gradients for several minutes This possibilitysignificantly increases the maximum possible ionosphere-inducederror for LAAS users beyond what was initially projected in workthat did not consider this masking possibility (eg [37]) It alsoaffects the ionospheric anomaly simulation algorithms described in[2338] as the method for selecting the worst possible ionosphericfront from the point of view of a given satellite is now dictated by thetheoretical speed of the resulting IPP

The threat space shown in Fig 13 is used to represent whatthe ionosphere is potentially capable of during rare extreme-ionospheric-storm conditions based on the validated events observedin the ionospheric data analysis described in Sec V However inadopting this threat model for the present wemust recognize that thevalidated events that have occurred to date are few in number sincethey are driven by the most extreme storms that have impacted theAmerican sector and cover a limited geographical area withinCONUS due to constraints imposed by the CORS receiver sites andequipage It is possible that localized events severe enough topotentially threaten LAAS users have occurred in CONUS but arenot included in the data used to construct the threat space Thereforewhile this threat space is deemed final for the purposes of CAT ILAAS certification [39] the anomaly mitigation algorithms haveadjustable parameters in case future ionospheric storms or dataanalyses reveal gradients that fall outside this threat space Anotherpossibility is that better understanding of how the ionospherebehaves during storm events will allow us to reduce the conservatismin the model as well as develop a more physically accuratedescription of how ionospheric anomalies propagate through LGFand user observations

Users can be protected from these situations by inflating theprotection bound that is broadcast to exclude geometries that wouldlead to intolerably large positioning errors [2338] Even thoughthere is no evidence of such large gradients on nominal days withoutthe ability to detect ionospheric storms in real time that is possessedby WAAS LAAS must assume that the ionosphere may be in adisturbed state at all times As a result geometries that would beunsafe during a storm are disallowed even though severe storms arevery unlikely to be present thus sacrificing precision approachavailability One method for excluding geometries that achieves anacceptable level of availability is described in [23]

VII Conclusions

This paper represents the culmination of several years ofteamwork in conducting as comprehensive a search as possible forpast extreme ionospheric events inCONUS thatwould impact LAASuser safety We have identified the ionospheric threat to users ofLAAS parameterized a model to represent it and applied a two-phase method of ionospheric spatial-gradient analysis and validationto quantify the magnitude of these threats This method consists ofautomatic processing of dual-frequency GPS carrier-phase measure-ments of ionospheric delay combined with manual comparison tosingle-frequency code-carrier divergence measurements to searchfor an upper bound on possible anomalous ionospheric gradientsThe final threat space of the model is ultimately driven by the largestof these points found but the parameters of this model have gonethrough a number of revisions in magnitude throughout this processThese values represent our best-validated observations during whatare recognized within the ionospheric community as the mostextreme geomagnetic storms during and following the most recentsolar maximum

The most time-consuming aspect of ionospheric anomaly dataanalysis is manual review of the apparent anomalies output by the

Fig 13 Ionospheric gradient threat space anomalous ionospheric

gradients observed plotted in threat model space as a function of a) slope

and ground speed and b) elevation and slope

DATTA-BARUA ETAL 1149

automated-search software In themajority of cases it was evident tohuman eyes that receiver or postprocessing errors created an apparentionospheric gradient that was not real and these observations wereset aside However a significant number of events that could not bediscounted remained andwere subjected tomore detailed analysis byteam consensus

As a result of this work we have validated low-elevation spatialgradients in the ionospheric delay at L1 as high as 360 and413 mm=km at high elevation These observations were made withCORS network stations in northern Ohio during the 20 Nov 2003ionospheric storm Station redundancy rules out the possibility ofthese gradients being generated by faulty receivers or significanterrors in receiver bias estimation Simultaneous observations onother satellites at similar elevations were also unusually highTherefore we conclude that the largest gradients observed herewerenot due to nonionospheric causes

The impact of these gradients on WAAS precision approachintegrity is negligible due to the implementation of the WAASextreme storm detector (however WAAS precision approachavailability is dramatically affected when severe storms occur)Mitigating the safety threats posed by ionospheric storms to users ofLAAS is achieved by disallowing specific user satellite geometriesduring precision approach at the cost of somewhat reducing LAASCAT 1 availability

Acknowledgments

The authors thank the FAA LAAS Program Office for theirsupport in the completion of this effort We would also like to thankspecific individuals who helped us in this work including ToddWalter Juan Blanch and Shankar Ramakrishnan at Stanford JohnWarburton and Tom Dehel of the Federal Aviation AdministrationWilliam J Hughes Technical Center and Attila Komjathy of theNASA Jet Propulsion Laboratory

References

[1] Enge P ldquoLocal AreaAugmentation ofGPS for the PrecisionApproachof Aircraftrdquo Proceedings of the IEEE Vol 87 No 1 Jan 1999pp 111ndash132doi1011095736345

[2] Enge P Walter T Pullen S Kee C Chao Y C and Tsai Y JldquoWide Area Augmentation of the Global Positioning SystemrdquoProceedings of the IEEE Vol 84 No 8 Aug 1996 pp 1063ndash1088doi1011095533954

[3] Misra P and Enge P Global Positioning System Signals

Measurement and Performance 2nd ed GangandashJamuna LincolnMA 2006

[4] Tascione T F Introduction to the Space Environment KriegerMalabar FL 1994

[5] Klobuchar J ldquoIonospheric Effects on GPSrdquo Global Positioning

System Theory and Applications edited by B W Parkinson and J JSpilker Vol 1 Progress in Astronautics and Aeronautics AIAAWashington DC 1996 pp 485ndash515

[6] Datta-Barua S Mannucci A J Walter T and Enge P ldquoAltitudinalVariation of Midlatitude Localized TEC Enhancement from Ground-and Space-BasedMeasurementsrdquo Space Weather Vol 6 No S10D062008doi1010292008SW000396

[7] Roble R G and Ridley E C ldquoThermospherendashIonospherendashMesosphere Electrodynamics General Circulation Model (TIME-GCM) Equinox Solar Cycle Minimum Simulations (30ndash500 km)rdquoGeophysical Research Letters Vol 21 No 6 1994 pp 417ndash420doi10102993GL03391

[8] Crowley G et al ldquoGlobal ThermospherendashIonosphere Response toOnset of 20 November 2003Magnetic Stormrdquo Journal of GeophysicalResearch Vol 111 No A10S18 2006doi1010292005JA011518

[9] Huba J D Joyce G and Fedder J A ldquoSami2 is Another Model ofthe Ionosphere (SAMI2) A New Low-Latitude Ionosphere ModelrdquoJournal of Geophysical Research Vol 105 No A10 2000 pp 23035ndash23053doi1010292000JA000035

[10] Bilitza D ldquoInternational Reference Ionosphere 2000rdquo Radio ScienceVol 36 No 2 MarchndashApril 2001 pp 261ndash275

doi1010292000RS002432[11] PiXWangC Hajj GA RosenGWilson BD andBaileyG J

ldquoEstimation of ExB Drift Using a Global Assimilative IonosphericModel An Observation System Simulation Experimentrdquo Journal of

Geophysical Research Vol 108(A2) No 1075 2003doi1010292001JA009235

[12] Schunk R et al ldquoGlobal Assimilation of Ionospheric Measurements(GAIM)rdquo Radio Science Vol 39 No RS1S02 2004doi1010292002RS002794

[13] Bust G S et al ldquoFour-Dimensional GPS Imaging of Space WeatherStormsrdquo Space Weather Vol 5 No S02003 2007doi1010292006SW000237

[14] Klobuchar J A Doherty P H and El-Arini M B ldquoPotentialIonospheric Limitations to Wide-Area Differential GPSrdquo Proceedingsof the 16th International Technical Meeting of the Satellite Division of

The Institute Of Navigation (IONGPS 1993) Salt Lake City UT 1993pp 1245ndash1254

[15] Klobuchar J A and Kunches J ldquoEye on the Ionosphere The SpatialVariability of Ionospheric Range Delayrdquo GPS Solutions Vol 3 No 32000 pp 70ndash74doi101007PL00012808

[16] Walter T et al ldquoRobust Detection of Ionospheric IrregularitiesrdquoProceedings of IONGPS 2000 httpwaasstanfordedu~wwupapersgpsPDFtoddion00pdf Salt Lake City UT 2000 pp 209ndash218

[17] Christie J R I Ko P-Y Hansen A Dai D Pullen S Pervan Band Parkinson B ldquoThe Effects of Local Ionospheric Decorrelation onLAAS Theory and Experimental Resultsrdquo Proceedings of the Instituteof Navigation National Technical Meeting 1999 pp 769ndash777

[18] ldquoMinimum Operational Performance Standards for GPS Local AreaAugmentation System Airborne Equipmentrdquo Radio TechnicalCommission for Aeronautics Washington DC Publ DO-253C2008

[19] Lee J Pullen S Datta-Barua S and Enge P ldquoAssessment ofNominal Ionosphere Spatial Decorrelation for GPS-Based AircraftLanding Systemsrdquo Journal of Aircraft Vol 44 No 5 2007 pp 1662ndash1669doi102514128199

[20] Datta-Barua SWalter T Pullen S LuoM Blanch J and Enge PldquoUsing WAAS Ionospheric Data to Estimate LAAS Short BaselineGradientsrdquo Proceedings of the Institute of Navigation National

Technical Meeting 2002 pp 523ndash530[21] Luo M Pullen S Akos D Xie G Datta-Barua S Walter T and

Enge P ldquoAssessment of Ionospheric Impact on LAAS Using WAASSupertruth Datardquo Proceedings of the ION 58th Annual Meeting and

CIGTF 21st Guidance Test Symposium 2002 pp 175ndash186[22] Ene A Qiu D Luo M Pullen S and Enge P ldquoAComprehensive

Ionosphere Storm Data Analysis Method to Support LAAS ThreatModel Developmentrdquo Proceedings of the Institute of Navigation

National Technical Meeting 2005 pp 110ndash130[23] Ramakrishnan S Lee J Pullen S andEnge P ldquoTargetedEphemeris

Decorrelation Parameter Inflation for Improved LAAS AvailabilityDuring Severe Ionosphere Anomaliesrdquo Proceedings of the 2008

National TechnicalMeeting of the Institute of Navigation pp 354ndash366[24] Komjathy A L Sparks and A J Mannucci ldquoGenerating High

Precision Ionospheric Ground-Truth Measurementsrdquo US PatentNo 7289061 B2 filed 30 Oct 2007

[25] Datta-Barua S ldquoIonospheric Threats to the Integrity of Airborne GPSUsersrdquo PhD Dissertation Stanford Univ Palo Alto CA 2008

[26] Luo M Pullen S Dennis J Konno H Xie G Walter T Enge PDatta-Barua S and Dehel T ldquoLAAS Ionosphere Spatial GradientThreat Model and Impact of LGF and Airborne MonitoringrdquoProceedings of the 16th International TechnicalMeeting of the Satellite

Division of the Institute of Navigation IONGPSGNSS 2003 pp 2255ndash2274

[27] Luo M Pullen S Walter T and Enge P ldquoIonosphere SpatialGradient Threat for LAAS Mitigation and Tolerable Threat SpacerdquoProceedings of the 2004 National Technical Meeting of the Institute of

Navigation pp 490ndash501[28] National Geodetic Survey CORS Team ldquoCORS Continuously

Operating Reference Stationsrdquo httpwwwngsnoaagovCORS[retrieved 6 Jan 2009]

[29] National Geophysical Data Center ldquoSpace Physics Interactive DataResourcerdquo 2005 httpspidrngdcnoaagovspidr [retrieved1 March 2005]

[30] Sugiura M and Kamei T ldquoEquatorial Dst Index 1957ndash1986rdquoInternational Association of Geomagnetism and Aeronomy Vol 401991 pp 7ndash14

[31] Menvielle M and Berthelier A ldquoThe K-Derived Planetary IndicesDescription and Availabilityrdquo Reviews of Geophysics Vol 29 No 3

1150 DATTA-BARUA ETAL

confirm amaximum slope observed due to this event of 413 mm=kmat 2100 UT

B Case Study 2 Low Elevation

We also investigate data to show the largest-magnitude ion-ospheric gradients that we have validated at low elevation (less than15 deg) at 2130 UT Figure 11 is a plot of the dual-frequencymeasurements of slant ionospheric delay over time at these stationswhen observing SVN 26 to the northwest at about 12 deg elevation(not shown) The two stations FREO and WOOS (see inset of Fig 3for location) begin at 15 m and rise to over 45 m of slant delay as the

LOSs from these stations to SVN 26 pass through the filament ofenhanced ionospheric delay

We analyze the WOOSndashGARF pair of stations to estimate theionospheric spatial gradient between them The dual-frequency-based slope rIDF is plotted in blue on Fig 12 The slope rises from0 mm=km to nearly 400 mm=km as the delay for WOOS rises to45 m during passage through the anomalous region while GARFholds steady at about 15ndash18m There is a dual-frequency data gap forGARF at 2054 UT that contributes to the data gap inrIDF howeverthe outage is not simultaneous with the highest slope observedbetween this station pair

The dual-frequency estimate is compared with the single-frequency estimate of the ionospheric spatial gradientrICMC (greenon Fig 12) Continuous single-frequencymeasurements exist exceptat one epoch at 2052 UT at which time the L1 measurements had avisible cycle slip that was manually removed Based on theagreement between the two measurements we conclude that thehighest slope between the stationsWOOS and GARF occurs at 2120UT and is about 360 mm=km

C LAAS Ionospheric Threat Space

Using the method described in Sec V and illustrated in thesubsections above numerous other instances of high differentialdelay between receivers closer than 100 km were investigated frommultiple storm days in CONUS listed in Table 1 This was amultistage multiperson process spanning data from storms from

20 202 204 206 208 21 212 214 216 218 220

10

20

30

40Dual-frequencyZOB1 GARF SV38

Sla

nt

del

ay (

m)

ZOB1

GARF

20 202 204 206 208 21 212 214 216 218 22-200

0

200

400

600

Ion

o s

lop

e (m

mk

m)

20 202 204 206 208 21 212 214 216 218 2240

50

60

70

80

Ele

vati

on

(d

eg)

Time (hour of 11202003)

a)

b)

c)

Fig 9 Maximum ionospheric gradient observed at high elevation

a) dual-frequency estimates of slant ionospheric delay b) spatial rate of

change and c) elevation from JPL-processed CORS data vs UT hour for

receivers ZOB1 and GARF tracking SVN 38 at high elevation

20 202 204 206 208 21 212 214 216 218 22-50

0

50

100

150

200

250

300

350

400

450

Time (hour of 11202003)

Ion

o S

lop

e (m

mk

m)

ZOB1 and GARF Slopes Comparison SV38

DF Slope

L1 Slope

Fig 10 Spatial rate of change of ionospheric delay over time between

two stations in OhioMichigan cluster viewing SVN 38 at high elevation

from rIDF (blue) and rICMC (red)

207 208 209 21 211 212 213 2140

5

10

15

20

25

30

35

40

45

50Iono Delay for Selected Stations SV 26 Elevation100689ordm - 12078ordm

Time(Hour of 11202003)

Sla

nt Io

no D

elay

(m

)

FREO

WOOS

LSBN

GARFGUST

Fig 11 Dual-frequency estimates of slant ionospheric delay from JPL-processed CORS data vs UT hour for select stations in the Ohio

Michigan cluster tracking SVN 26 at low elevation

208 209 21 211 212 213 214-50

0

50

100

150

200

250

300

350

400

Time (hour of 11202003)

Ion

o S

lop

e (m

mk

m)

WOOS and GARF Slopes Comparison SV26

DF Slope

L1 Slope

Fig 12 Spatial rate of change of ionospheric delay over time between

two stations inOhioMichigan cluster viewing SVN26at lowelevation as

from rIDF (blue) and rICMC (green)

1148 DATTA-BARUA ETAL

2000ndash2005 during the maximum and waning phases of the mostrecent solar cycle

Figure 13 summarizes the results of this data analysis process bypopulating the threat space with cases observed in CORS data fromthe storms examined In Fig 13a the instances of anomalousgradients are plotted as a function of slope (mm=km) on thehorizontal axis and ground speed (m=s) on the vertical axis Thepoints that were identified in dual-frequency data and subsequentlyverified with single-frequency L1 code-minus-carrier data aremarked with diamonds The points that were estimated from therICMC directly are shown as triangles These same points are plottedin Fig 13b as a function of elevation angle (degrees) on the horizontalaxis and slope (mm=km) on the vertical axis

Note that width estimates do not appear in this figure Thewidth ofthe TEC rampwas generally difficult to assess from the available datagiven the typical 50ndash100 km separation of CORS reference stationsIn addition the integrity results (ie the threat model impacts onLAAS users) were not found to be sensitive to this parameter [27]

Empirical bounds are fitted to the points shown in Fig 13 in orderto limit the threat space to the maximum gradients discovered andvalidated These bounds are marked with solid lines in Fig 13Figure 13a shows that for events moving at speeds less than 90 mswith respect to the ground a maximum slope of 150 mmkm boundsthe events observed Figure 13b shows the upper bound on themaximum gradient of this threat model as a function of satelliteelevation angle for speeds greater than 90 ms a slope of375 mm=km at low elevation increasing linearly at a rate of 1 mmkm from 15 to 65 deg elevation to reach 425 mm=km at highelevation For ground speeds below 90 ms the maximum slope is150mmkm independent of elevation These bounds slightly exceedthe largest gradients validated from the data analysis due to marginadded to account for measurement error In addition to the plottedmaximum gradient and speed bounds width (distance between highand low delay regions) is delimited to be between 25 and 200 km andtotal slant differential delay up to 50 m [2236]

Figure 13a shows a maximum estimated ionospheric front speedof about 600 m=s with respect to the ground and the speed was

limited to be nomore than 750 m=s This bound is based on a numberof considerations First in practice the absolute speed is of secondaryimportance to the front speedwith respect to the aircraft andLGF ray-paths (or effectively their IPPs) The relative motion permitsdetectability in fact the estimates of speed were very sensitive to thisrelative motion There is a real (albeit unlikely) possibility that IPPspeed might cancel out the actual ionospheric front speed relative tothe ground and thus prevent time-based LGF monitoring fromdetecting very large gradients for several minutes This possibilitysignificantly increases the maximum possible ionosphere-inducederror for LAAS users beyond what was initially projected in workthat did not consider this masking possibility (eg [37]) It alsoaffects the ionospheric anomaly simulation algorithms described in[2338] as the method for selecting the worst possible ionosphericfront from the point of view of a given satellite is now dictated by thetheoretical speed of the resulting IPP

The threat space shown in Fig 13 is used to represent whatthe ionosphere is potentially capable of during rare extreme-ionospheric-storm conditions based on the validated events observedin the ionospheric data analysis described in Sec V However inadopting this threat model for the present wemust recognize that thevalidated events that have occurred to date are few in number sincethey are driven by the most extreme storms that have impacted theAmerican sector and cover a limited geographical area withinCONUS due to constraints imposed by the CORS receiver sites andequipage It is possible that localized events severe enough topotentially threaten LAAS users have occurred in CONUS but arenot included in the data used to construct the threat space Thereforewhile this threat space is deemed final for the purposes of CAT ILAAS certification [39] the anomaly mitigation algorithms haveadjustable parameters in case future ionospheric storms or dataanalyses reveal gradients that fall outside this threat space Anotherpossibility is that better understanding of how the ionospherebehaves during storm events will allow us to reduce the conservatismin the model as well as develop a more physically accuratedescription of how ionospheric anomalies propagate through LGFand user observations

Users can be protected from these situations by inflating theprotection bound that is broadcast to exclude geometries that wouldlead to intolerably large positioning errors [2338] Even thoughthere is no evidence of such large gradients on nominal days withoutthe ability to detect ionospheric storms in real time that is possessedby WAAS LAAS must assume that the ionosphere may be in adisturbed state at all times As a result geometries that would beunsafe during a storm are disallowed even though severe storms arevery unlikely to be present thus sacrificing precision approachavailability One method for excluding geometries that achieves anacceptable level of availability is described in [23]

VII Conclusions

This paper represents the culmination of several years ofteamwork in conducting as comprehensive a search as possible forpast extreme ionospheric events inCONUS thatwould impact LAASuser safety We have identified the ionospheric threat to users ofLAAS parameterized a model to represent it and applied a two-phase method of ionospheric spatial-gradient analysis and validationto quantify the magnitude of these threats This method consists ofautomatic processing of dual-frequency GPS carrier-phase measure-ments of ionospheric delay combined with manual comparison tosingle-frequency code-carrier divergence measurements to searchfor an upper bound on possible anomalous ionospheric gradientsThe final threat space of the model is ultimately driven by the largestof these points found but the parameters of this model have gonethrough a number of revisions in magnitude throughout this processThese values represent our best-validated observations during whatare recognized within the ionospheric community as the mostextreme geomagnetic storms during and following the most recentsolar maximum

The most time-consuming aspect of ionospheric anomaly dataanalysis is manual review of the apparent anomalies output by the

Fig 13 Ionospheric gradient threat space anomalous ionospheric

gradients observed plotted in threat model space as a function of a) slope

and ground speed and b) elevation and slope

DATTA-BARUA ETAL 1149

automated-search software In themajority of cases it was evident tohuman eyes that receiver or postprocessing errors created an apparentionospheric gradient that was not real and these observations wereset aside However a significant number of events that could not bediscounted remained andwere subjected tomore detailed analysis byteam consensus

As a result of this work we have validated low-elevation spatialgradients in the ionospheric delay at L1 as high as 360 and413 mm=km at high elevation These observations were made withCORS network stations in northern Ohio during the 20 Nov 2003ionospheric storm Station redundancy rules out the possibility ofthese gradients being generated by faulty receivers or significanterrors in receiver bias estimation Simultaneous observations onother satellites at similar elevations were also unusually highTherefore we conclude that the largest gradients observed herewerenot due to nonionospheric causes

The impact of these gradients on WAAS precision approachintegrity is negligible due to the implementation of the WAASextreme storm detector (however WAAS precision approachavailability is dramatically affected when severe storms occur)Mitigating the safety threats posed by ionospheric storms to users ofLAAS is achieved by disallowing specific user satellite geometriesduring precision approach at the cost of somewhat reducing LAASCAT 1 availability

Acknowledgments

The authors thank the FAA LAAS Program Office for theirsupport in the completion of this effort We would also like to thankspecific individuals who helped us in this work including ToddWalter Juan Blanch and Shankar Ramakrishnan at Stanford JohnWarburton and Tom Dehel of the Federal Aviation AdministrationWilliam J Hughes Technical Center and Attila Komjathy of theNASA Jet Propulsion Laboratory

References

[1] Enge P ldquoLocal AreaAugmentation ofGPS for the PrecisionApproachof Aircraftrdquo Proceedings of the IEEE Vol 87 No 1 Jan 1999pp 111ndash132doi1011095736345

[2] Enge P Walter T Pullen S Kee C Chao Y C and Tsai Y JldquoWide Area Augmentation of the Global Positioning SystemrdquoProceedings of the IEEE Vol 84 No 8 Aug 1996 pp 1063ndash1088doi1011095533954

[3] Misra P and Enge P Global Positioning System Signals

Measurement and Performance 2nd ed GangandashJamuna LincolnMA 2006

[4] Tascione T F Introduction to the Space Environment KriegerMalabar FL 1994

[5] Klobuchar J ldquoIonospheric Effects on GPSrdquo Global Positioning

System Theory and Applications edited by B W Parkinson and J JSpilker Vol 1 Progress in Astronautics and Aeronautics AIAAWashington DC 1996 pp 485ndash515

[6] Datta-Barua S Mannucci A J Walter T and Enge P ldquoAltitudinalVariation of Midlatitude Localized TEC Enhancement from Ground-and Space-BasedMeasurementsrdquo Space Weather Vol 6 No S10D062008doi1010292008SW000396

[7] Roble R G and Ridley E C ldquoThermospherendashIonospherendashMesosphere Electrodynamics General Circulation Model (TIME-GCM) Equinox Solar Cycle Minimum Simulations (30ndash500 km)rdquoGeophysical Research Letters Vol 21 No 6 1994 pp 417ndash420doi10102993GL03391

[8] Crowley G et al ldquoGlobal ThermospherendashIonosphere Response toOnset of 20 November 2003Magnetic Stormrdquo Journal of GeophysicalResearch Vol 111 No A10S18 2006doi1010292005JA011518

[9] Huba J D Joyce G and Fedder J A ldquoSami2 is Another Model ofthe Ionosphere (SAMI2) A New Low-Latitude Ionosphere ModelrdquoJournal of Geophysical Research Vol 105 No A10 2000 pp 23035ndash23053doi1010292000JA000035

[10] Bilitza D ldquoInternational Reference Ionosphere 2000rdquo Radio ScienceVol 36 No 2 MarchndashApril 2001 pp 261ndash275

doi1010292000RS002432[11] PiXWangC Hajj GA RosenGWilson BD andBaileyG J

ldquoEstimation of ExB Drift Using a Global Assimilative IonosphericModel An Observation System Simulation Experimentrdquo Journal of

Geophysical Research Vol 108(A2) No 1075 2003doi1010292001JA009235

[12] Schunk R et al ldquoGlobal Assimilation of Ionospheric Measurements(GAIM)rdquo Radio Science Vol 39 No RS1S02 2004doi1010292002RS002794

[13] Bust G S et al ldquoFour-Dimensional GPS Imaging of Space WeatherStormsrdquo Space Weather Vol 5 No S02003 2007doi1010292006SW000237

[14] Klobuchar J A Doherty P H and El-Arini M B ldquoPotentialIonospheric Limitations to Wide-Area Differential GPSrdquo Proceedingsof the 16th International Technical Meeting of the Satellite Division of

The Institute Of Navigation (IONGPS 1993) Salt Lake City UT 1993pp 1245ndash1254

[15] Klobuchar J A and Kunches J ldquoEye on the Ionosphere The SpatialVariability of Ionospheric Range Delayrdquo GPS Solutions Vol 3 No 32000 pp 70ndash74doi101007PL00012808

[16] Walter T et al ldquoRobust Detection of Ionospheric IrregularitiesrdquoProceedings of IONGPS 2000 httpwaasstanfordedu~wwupapersgpsPDFtoddion00pdf Salt Lake City UT 2000 pp 209ndash218

[17] Christie J R I Ko P-Y Hansen A Dai D Pullen S Pervan Band Parkinson B ldquoThe Effects of Local Ionospheric Decorrelation onLAAS Theory and Experimental Resultsrdquo Proceedings of the Instituteof Navigation National Technical Meeting 1999 pp 769ndash777

[18] ldquoMinimum Operational Performance Standards for GPS Local AreaAugmentation System Airborne Equipmentrdquo Radio TechnicalCommission for Aeronautics Washington DC Publ DO-253C2008

[19] Lee J Pullen S Datta-Barua S and Enge P ldquoAssessment ofNominal Ionosphere Spatial Decorrelation for GPS-Based AircraftLanding Systemsrdquo Journal of Aircraft Vol 44 No 5 2007 pp 1662ndash1669doi102514128199

[20] Datta-Barua SWalter T Pullen S LuoM Blanch J and Enge PldquoUsing WAAS Ionospheric Data to Estimate LAAS Short BaselineGradientsrdquo Proceedings of the Institute of Navigation National

Technical Meeting 2002 pp 523ndash530[21] Luo M Pullen S Akos D Xie G Datta-Barua S Walter T and

Enge P ldquoAssessment of Ionospheric Impact on LAAS Using WAASSupertruth Datardquo Proceedings of the ION 58th Annual Meeting and

CIGTF 21st Guidance Test Symposium 2002 pp 175ndash186[22] Ene A Qiu D Luo M Pullen S and Enge P ldquoAComprehensive

Ionosphere Storm Data Analysis Method to Support LAAS ThreatModel Developmentrdquo Proceedings of the Institute of Navigation

National Technical Meeting 2005 pp 110ndash130[23] Ramakrishnan S Lee J Pullen S andEnge P ldquoTargetedEphemeris

Decorrelation Parameter Inflation for Improved LAAS AvailabilityDuring Severe Ionosphere Anomaliesrdquo Proceedings of the 2008

National TechnicalMeeting of the Institute of Navigation pp 354ndash366[24] Komjathy A L Sparks and A J Mannucci ldquoGenerating High

Precision Ionospheric Ground-Truth Measurementsrdquo US PatentNo 7289061 B2 filed 30 Oct 2007

[25] Datta-Barua S ldquoIonospheric Threats to the Integrity of Airborne GPSUsersrdquo PhD Dissertation Stanford Univ Palo Alto CA 2008

[26] Luo M Pullen S Dennis J Konno H Xie G Walter T Enge PDatta-Barua S and Dehel T ldquoLAAS Ionosphere Spatial GradientThreat Model and Impact of LGF and Airborne MonitoringrdquoProceedings of the 16th International TechnicalMeeting of the Satellite

Division of the Institute of Navigation IONGPSGNSS 2003 pp 2255ndash2274

[27] Luo M Pullen S Walter T and Enge P ldquoIonosphere SpatialGradient Threat for LAAS Mitigation and Tolerable Threat SpacerdquoProceedings of the 2004 National Technical Meeting of the Institute of

Navigation pp 490ndash501[28] National Geodetic Survey CORS Team ldquoCORS Continuously

Operating Reference Stationsrdquo httpwwwngsnoaagovCORS[retrieved 6 Jan 2009]

[29] National Geophysical Data Center ldquoSpace Physics Interactive DataResourcerdquo 2005 httpspidrngdcnoaagovspidr [retrieved1 March 2005]

[30] Sugiura M and Kamei T ldquoEquatorial Dst Index 1957ndash1986rdquoInternational Association of Geomagnetism and Aeronomy Vol 401991 pp 7ndash14

[31] Menvielle M and Berthelier A ldquoThe K-Derived Planetary IndicesDescription and Availabilityrdquo Reviews of Geophysics Vol 29 No 3

1150 DATTA-BARUA ETAL

2000ndash2005 during the maximum and waning phases of the mostrecent solar cycle

Figure 13 summarizes the results of this data analysis process bypopulating the threat space with cases observed in CORS data fromthe storms examined In Fig 13a the instances of anomalousgradients are plotted as a function of slope (mm=km) on thehorizontal axis and ground speed (m=s) on the vertical axis Thepoints that were identified in dual-frequency data and subsequentlyverified with single-frequency L1 code-minus-carrier data aremarked with diamonds The points that were estimated from therICMC directly are shown as triangles These same points are plottedin Fig 13b as a function of elevation angle (degrees) on the horizontalaxis and slope (mm=km) on the vertical axis

Note that width estimates do not appear in this figure Thewidth ofthe TEC rampwas generally difficult to assess from the available datagiven the typical 50ndash100 km separation of CORS reference stationsIn addition the integrity results (ie the threat model impacts onLAAS users) were not found to be sensitive to this parameter [27]

Empirical bounds are fitted to the points shown in Fig 13 in orderto limit the threat space to the maximum gradients discovered andvalidated These bounds are marked with solid lines in Fig 13Figure 13a shows that for events moving at speeds less than 90 mswith respect to the ground a maximum slope of 150 mmkm boundsthe events observed Figure 13b shows the upper bound on themaximum gradient of this threat model as a function of satelliteelevation angle for speeds greater than 90 ms a slope of375 mm=km at low elevation increasing linearly at a rate of 1 mmkm from 15 to 65 deg elevation to reach 425 mm=km at highelevation For ground speeds below 90 ms the maximum slope is150mmkm independent of elevation These bounds slightly exceedthe largest gradients validated from the data analysis due to marginadded to account for measurement error In addition to the plottedmaximum gradient and speed bounds width (distance between highand low delay regions) is delimited to be between 25 and 200 km andtotal slant differential delay up to 50 m [2236]

Figure 13a shows a maximum estimated ionospheric front speedof about 600 m=s with respect to the ground and the speed was

limited to be nomore than 750 m=s This bound is based on a numberof considerations First in practice the absolute speed is of secondaryimportance to the front speedwith respect to the aircraft andLGF ray-paths (or effectively their IPPs) The relative motion permitsdetectability in fact the estimates of speed were very sensitive to thisrelative motion There is a real (albeit unlikely) possibility that IPPspeed might cancel out the actual ionospheric front speed relative tothe ground and thus prevent time-based LGF monitoring fromdetecting very large gradients for several minutes This possibilitysignificantly increases the maximum possible ionosphere-inducederror for LAAS users beyond what was initially projected in workthat did not consider this masking possibility (eg [37]) It alsoaffects the ionospheric anomaly simulation algorithms described in[2338] as the method for selecting the worst possible ionosphericfront from the point of view of a given satellite is now dictated by thetheoretical speed of the resulting IPP

The threat space shown in Fig 13 is used to represent whatthe ionosphere is potentially capable of during rare extreme-ionospheric-storm conditions based on the validated events observedin the ionospheric data analysis described in Sec V However inadopting this threat model for the present wemust recognize that thevalidated events that have occurred to date are few in number sincethey are driven by the most extreme storms that have impacted theAmerican sector and cover a limited geographical area withinCONUS due to constraints imposed by the CORS receiver sites andequipage It is possible that localized events severe enough topotentially threaten LAAS users have occurred in CONUS but arenot included in the data used to construct the threat space Thereforewhile this threat space is deemed final for the purposes of CAT ILAAS certification [39] the anomaly mitigation algorithms haveadjustable parameters in case future ionospheric storms or dataanalyses reveal gradients that fall outside this threat space Anotherpossibility is that better understanding of how the ionospherebehaves during storm events will allow us to reduce the conservatismin the model as well as develop a more physically accuratedescription of how ionospheric anomalies propagate through LGFand user observations

Users can be protected from these situations by inflating theprotection bound that is broadcast to exclude geometries that wouldlead to intolerably large positioning errors [2338] Even thoughthere is no evidence of such large gradients on nominal days withoutthe ability to detect ionospheric storms in real time that is possessedby WAAS LAAS must assume that the ionosphere may be in adisturbed state at all times As a result geometries that would beunsafe during a storm are disallowed even though severe storms arevery unlikely to be present thus sacrificing precision approachavailability One method for excluding geometries that achieves anacceptable level of availability is described in [23]

VII Conclusions

This paper represents the culmination of several years ofteamwork in conducting as comprehensive a search as possible forpast extreme ionospheric events inCONUS thatwould impact LAASuser safety We have identified the ionospheric threat to users ofLAAS parameterized a model to represent it and applied a two-phase method of ionospheric spatial-gradient analysis and validationto quantify the magnitude of these threats This method consists ofautomatic processing of dual-frequency GPS carrier-phase measure-ments of ionospheric delay combined with manual comparison tosingle-frequency code-carrier divergence measurements to searchfor an upper bound on possible anomalous ionospheric gradientsThe final threat space of the model is ultimately driven by the largestof these points found but the parameters of this model have gonethrough a number of revisions in magnitude throughout this processThese values represent our best-validated observations during whatare recognized within the ionospheric community as the mostextreme geomagnetic storms during and following the most recentsolar maximum

The most time-consuming aspect of ionospheric anomaly dataanalysis is manual review of the apparent anomalies output by the

Fig 13 Ionospheric gradient threat space anomalous ionospheric

gradients observed plotted in threat model space as a function of a) slope

and ground speed and b) elevation and slope

DATTA-BARUA ETAL 1149

automated-search software In themajority of cases it was evident tohuman eyes that receiver or postprocessing errors created an apparentionospheric gradient that was not real and these observations wereset aside However a significant number of events that could not bediscounted remained andwere subjected tomore detailed analysis byteam consensus

As a result of this work we have validated low-elevation spatialgradients in the ionospheric delay at L1 as high as 360 and413 mm=km at high elevation These observations were made withCORS network stations in northern Ohio during the 20 Nov 2003ionospheric storm Station redundancy rules out the possibility ofthese gradients being generated by faulty receivers or significanterrors in receiver bias estimation Simultaneous observations onother satellites at similar elevations were also unusually highTherefore we conclude that the largest gradients observed herewerenot due to nonionospheric causes

The impact of these gradients on WAAS precision approachintegrity is negligible due to the implementation of the WAASextreme storm detector (however WAAS precision approachavailability is dramatically affected when severe storms occur)Mitigating the safety threats posed by ionospheric storms to users ofLAAS is achieved by disallowing specific user satellite geometriesduring precision approach at the cost of somewhat reducing LAASCAT 1 availability

Acknowledgments

The authors thank the FAA LAAS Program Office for theirsupport in the completion of this effort We would also like to thankspecific individuals who helped us in this work including ToddWalter Juan Blanch and Shankar Ramakrishnan at Stanford JohnWarburton and Tom Dehel of the Federal Aviation AdministrationWilliam J Hughes Technical Center and Attila Komjathy of theNASA Jet Propulsion Laboratory

References

[1] Enge P ldquoLocal AreaAugmentation ofGPS for the PrecisionApproachof Aircraftrdquo Proceedings of the IEEE Vol 87 No 1 Jan 1999pp 111ndash132doi1011095736345

[2] Enge P Walter T Pullen S Kee C Chao Y C and Tsai Y JldquoWide Area Augmentation of the Global Positioning SystemrdquoProceedings of the IEEE Vol 84 No 8 Aug 1996 pp 1063ndash1088doi1011095533954

[3] Misra P and Enge P Global Positioning System Signals

Measurement and Performance 2nd ed GangandashJamuna LincolnMA 2006

[4] Tascione T F Introduction to the Space Environment KriegerMalabar FL 1994

[5] Klobuchar J ldquoIonospheric Effects on GPSrdquo Global Positioning

System Theory and Applications edited by B W Parkinson and J JSpilker Vol 1 Progress in Astronautics and Aeronautics AIAAWashington DC 1996 pp 485ndash515

[6] Datta-Barua S Mannucci A J Walter T and Enge P ldquoAltitudinalVariation of Midlatitude Localized TEC Enhancement from Ground-and Space-BasedMeasurementsrdquo Space Weather Vol 6 No S10D062008doi1010292008SW000396

[7] Roble R G and Ridley E C ldquoThermospherendashIonospherendashMesosphere Electrodynamics General Circulation Model (TIME-GCM) Equinox Solar Cycle Minimum Simulations (30ndash500 km)rdquoGeophysical Research Letters Vol 21 No 6 1994 pp 417ndash420doi10102993GL03391

[8] Crowley G et al ldquoGlobal ThermospherendashIonosphere Response toOnset of 20 November 2003Magnetic Stormrdquo Journal of GeophysicalResearch Vol 111 No A10S18 2006doi1010292005JA011518

[9] Huba J D Joyce G and Fedder J A ldquoSami2 is Another Model ofthe Ionosphere (SAMI2) A New Low-Latitude Ionosphere ModelrdquoJournal of Geophysical Research Vol 105 No A10 2000 pp 23035ndash23053doi1010292000JA000035

[10] Bilitza D ldquoInternational Reference Ionosphere 2000rdquo Radio ScienceVol 36 No 2 MarchndashApril 2001 pp 261ndash275

doi1010292000RS002432[11] PiXWangC Hajj GA RosenGWilson BD andBaileyG J

ldquoEstimation of ExB Drift Using a Global Assimilative IonosphericModel An Observation System Simulation Experimentrdquo Journal of

Geophysical Research Vol 108(A2) No 1075 2003doi1010292001JA009235

[12] Schunk R et al ldquoGlobal Assimilation of Ionospheric Measurements(GAIM)rdquo Radio Science Vol 39 No RS1S02 2004doi1010292002RS002794

[13] Bust G S et al ldquoFour-Dimensional GPS Imaging of Space WeatherStormsrdquo Space Weather Vol 5 No S02003 2007doi1010292006SW000237

[14] Klobuchar J A Doherty P H and El-Arini M B ldquoPotentialIonospheric Limitations to Wide-Area Differential GPSrdquo Proceedingsof the 16th International Technical Meeting of the Satellite Division of

The Institute Of Navigation (IONGPS 1993) Salt Lake City UT 1993pp 1245ndash1254

[15] Klobuchar J A and Kunches J ldquoEye on the Ionosphere The SpatialVariability of Ionospheric Range Delayrdquo GPS Solutions Vol 3 No 32000 pp 70ndash74doi101007PL00012808

[16] Walter T et al ldquoRobust Detection of Ionospheric IrregularitiesrdquoProceedings of IONGPS 2000 httpwaasstanfordedu~wwupapersgpsPDFtoddion00pdf Salt Lake City UT 2000 pp 209ndash218

[17] Christie J R I Ko P-Y Hansen A Dai D Pullen S Pervan Band Parkinson B ldquoThe Effects of Local Ionospheric Decorrelation onLAAS Theory and Experimental Resultsrdquo Proceedings of the Instituteof Navigation National Technical Meeting 1999 pp 769ndash777

[18] ldquoMinimum Operational Performance Standards for GPS Local AreaAugmentation System Airborne Equipmentrdquo Radio TechnicalCommission for Aeronautics Washington DC Publ DO-253C2008

[19] Lee J Pullen S Datta-Barua S and Enge P ldquoAssessment ofNominal Ionosphere Spatial Decorrelation for GPS-Based AircraftLanding Systemsrdquo Journal of Aircraft Vol 44 No 5 2007 pp 1662ndash1669doi102514128199

[20] Datta-Barua SWalter T Pullen S LuoM Blanch J and Enge PldquoUsing WAAS Ionospheric Data to Estimate LAAS Short BaselineGradientsrdquo Proceedings of the Institute of Navigation National

Technical Meeting 2002 pp 523ndash530[21] Luo M Pullen S Akos D Xie G Datta-Barua S Walter T and

Enge P ldquoAssessment of Ionospheric Impact on LAAS Using WAASSupertruth Datardquo Proceedings of the ION 58th Annual Meeting and

CIGTF 21st Guidance Test Symposium 2002 pp 175ndash186[22] Ene A Qiu D Luo M Pullen S and Enge P ldquoAComprehensive

Ionosphere Storm Data Analysis Method to Support LAAS ThreatModel Developmentrdquo Proceedings of the Institute of Navigation

National Technical Meeting 2005 pp 110ndash130[23] Ramakrishnan S Lee J Pullen S andEnge P ldquoTargetedEphemeris

Decorrelation Parameter Inflation for Improved LAAS AvailabilityDuring Severe Ionosphere Anomaliesrdquo Proceedings of the 2008

National TechnicalMeeting of the Institute of Navigation pp 354ndash366[24] Komjathy A L Sparks and A J Mannucci ldquoGenerating High

Precision Ionospheric Ground-Truth Measurementsrdquo US PatentNo 7289061 B2 filed 30 Oct 2007

[25] Datta-Barua S ldquoIonospheric Threats to the Integrity of Airborne GPSUsersrdquo PhD Dissertation Stanford Univ Palo Alto CA 2008

[26] Luo M Pullen S Dennis J Konno H Xie G Walter T Enge PDatta-Barua S and Dehel T ldquoLAAS Ionosphere Spatial GradientThreat Model and Impact of LGF and Airborne MonitoringrdquoProceedings of the 16th International TechnicalMeeting of the Satellite

Division of the Institute of Navigation IONGPSGNSS 2003 pp 2255ndash2274

[27] Luo M Pullen S Walter T and Enge P ldquoIonosphere SpatialGradient Threat for LAAS Mitigation and Tolerable Threat SpacerdquoProceedings of the 2004 National Technical Meeting of the Institute of

Navigation pp 490ndash501[28] National Geodetic Survey CORS Team ldquoCORS Continuously

Operating Reference Stationsrdquo httpwwwngsnoaagovCORS[retrieved 6 Jan 2009]

[29] National Geophysical Data Center ldquoSpace Physics Interactive DataResourcerdquo 2005 httpspidrngdcnoaagovspidr [retrieved1 March 2005]

[30] Sugiura M and Kamei T ldquoEquatorial Dst Index 1957ndash1986rdquoInternational Association of Geomagnetism and Aeronomy Vol 401991 pp 7ndash14

[31] Menvielle M and Berthelier A ldquoThe K-Derived Planetary IndicesDescription and Availabilityrdquo Reviews of Geophysics Vol 29 No 3

1150 DATTA-BARUA ETAL

automated-search software In themajority of cases it was evident tohuman eyes that receiver or postprocessing errors created an apparentionospheric gradient that was not real and these observations wereset aside However a significant number of events that could not bediscounted remained andwere subjected tomore detailed analysis byteam consensus

As a result of this work we have validated low-elevation spatialgradients in the ionospheric delay at L1 as high as 360 and413 mm=km at high elevation These observations were made withCORS network stations in northern Ohio during the 20 Nov 2003ionospheric storm Station redundancy rules out the possibility ofthese gradients being generated by faulty receivers or significanterrors in receiver bias estimation Simultaneous observations onother satellites at similar elevations were also unusually highTherefore we conclude that the largest gradients observed herewerenot due to nonionospheric causes

The impact of these gradients on WAAS precision approachintegrity is negligible due to the implementation of the WAASextreme storm detector (however WAAS precision approachavailability is dramatically affected when severe storms occur)Mitigating the safety threats posed by ionospheric storms to users ofLAAS is achieved by disallowing specific user satellite geometriesduring precision approach at the cost of somewhat reducing LAASCAT 1 availability

Acknowledgments

The authors thank the FAA LAAS Program Office for theirsupport in the completion of this effort We would also like to thankspecific individuals who helped us in this work including ToddWalter Juan Blanch and Shankar Ramakrishnan at Stanford JohnWarburton and Tom Dehel of the Federal Aviation AdministrationWilliam J Hughes Technical Center and Attila Komjathy of theNASA Jet Propulsion Laboratory

References

[1] Enge P ldquoLocal AreaAugmentation ofGPS for the PrecisionApproachof Aircraftrdquo Proceedings of the IEEE Vol 87 No 1 Jan 1999pp 111ndash132doi1011095736345

[2] Enge P Walter T Pullen S Kee C Chao Y C and Tsai Y JldquoWide Area Augmentation of the Global Positioning SystemrdquoProceedings of the IEEE Vol 84 No 8 Aug 1996 pp 1063ndash1088doi1011095533954

[3] Misra P and Enge P Global Positioning System Signals

Measurement and Performance 2nd ed GangandashJamuna LincolnMA 2006

[4] Tascione T F Introduction to the Space Environment KriegerMalabar FL 1994

[5] Klobuchar J ldquoIonospheric Effects on GPSrdquo Global Positioning

System Theory and Applications edited by B W Parkinson and J JSpilker Vol 1 Progress in Astronautics and Aeronautics AIAAWashington DC 1996 pp 485ndash515

[6] Datta-Barua S Mannucci A J Walter T and Enge P ldquoAltitudinalVariation of Midlatitude Localized TEC Enhancement from Ground-and Space-BasedMeasurementsrdquo Space Weather Vol 6 No S10D062008doi1010292008SW000396

[7] Roble R G and Ridley E C ldquoThermospherendashIonospherendashMesosphere Electrodynamics General Circulation Model (TIME-GCM) Equinox Solar Cycle Minimum Simulations (30ndash500 km)rdquoGeophysical Research Letters Vol 21 No 6 1994 pp 417ndash420doi10102993GL03391

[8] Crowley G et al ldquoGlobal ThermospherendashIonosphere Response toOnset of 20 November 2003Magnetic Stormrdquo Journal of GeophysicalResearch Vol 111 No A10S18 2006doi1010292005JA011518

[9] Huba J D Joyce G and Fedder J A ldquoSami2 is Another Model ofthe Ionosphere (SAMI2) A New Low-Latitude Ionosphere ModelrdquoJournal of Geophysical Research Vol 105 No A10 2000 pp 23035ndash23053doi1010292000JA000035

[10] Bilitza D ldquoInternational Reference Ionosphere 2000rdquo Radio ScienceVol 36 No 2 MarchndashApril 2001 pp 261ndash275

doi1010292000RS002432[11] PiXWangC Hajj GA RosenGWilson BD andBaileyG J

ldquoEstimation of ExB Drift Using a Global Assimilative IonosphericModel An Observation System Simulation Experimentrdquo Journal of

Geophysical Research Vol 108(A2) No 1075 2003doi1010292001JA009235

[12] Schunk R et al ldquoGlobal Assimilation of Ionospheric Measurements(GAIM)rdquo Radio Science Vol 39 No RS1S02 2004doi1010292002RS002794

[13] Bust G S et al ldquoFour-Dimensional GPS Imaging of Space WeatherStormsrdquo Space Weather Vol 5 No S02003 2007doi1010292006SW000237

[14] Klobuchar J A Doherty P H and El-Arini M B ldquoPotentialIonospheric Limitations to Wide-Area Differential GPSrdquo Proceedingsof the 16th International Technical Meeting of the Satellite Division of

The Institute Of Navigation (IONGPS 1993) Salt Lake City UT 1993pp 1245ndash1254

[15] Klobuchar J A and Kunches J ldquoEye on the Ionosphere The SpatialVariability of Ionospheric Range Delayrdquo GPS Solutions Vol 3 No 32000 pp 70ndash74doi101007PL00012808

[16] Walter T et al ldquoRobust Detection of Ionospheric IrregularitiesrdquoProceedings of IONGPS 2000 httpwaasstanfordedu~wwupapersgpsPDFtoddion00pdf Salt Lake City UT 2000 pp 209ndash218

[17] Christie J R I Ko P-Y Hansen A Dai D Pullen S Pervan Band Parkinson B ldquoThe Effects of Local Ionospheric Decorrelation onLAAS Theory and Experimental Resultsrdquo Proceedings of the Instituteof Navigation National Technical Meeting 1999 pp 769ndash777

[18] ldquoMinimum Operational Performance Standards for GPS Local AreaAugmentation System Airborne Equipmentrdquo Radio TechnicalCommission for Aeronautics Washington DC Publ DO-253C2008

[19] Lee J Pullen S Datta-Barua S and Enge P ldquoAssessment ofNominal Ionosphere Spatial Decorrelation for GPS-Based AircraftLanding Systemsrdquo Journal of Aircraft Vol 44 No 5 2007 pp 1662ndash1669doi102514128199

[20] Datta-Barua SWalter T Pullen S LuoM Blanch J and Enge PldquoUsing WAAS Ionospheric Data to Estimate LAAS Short BaselineGradientsrdquo Proceedings of the Institute of Navigation National

Technical Meeting 2002 pp 523ndash530[21] Luo M Pullen S Akos D Xie G Datta-Barua S Walter T and

Enge P ldquoAssessment of Ionospheric Impact on LAAS Using WAASSupertruth Datardquo Proceedings of the ION 58th Annual Meeting and

CIGTF 21st Guidance Test Symposium 2002 pp 175ndash186[22] Ene A Qiu D Luo M Pullen S and Enge P ldquoAComprehensive

Ionosphere Storm Data Analysis Method to Support LAAS ThreatModel Developmentrdquo Proceedings of the Institute of Navigation

National Technical Meeting 2005 pp 110ndash130[23] Ramakrishnan S Lee J Pullen S andEnge P ldquoTargetedEphemeris

Decorrelation Parameter Inflation for Improved LAAS AvailabilityDuring Severe Ionosphere Anomaliesrdquo Proceedings of the 2008

National TechnicalMeeting of the Institute of Navigation pp 354ndash366[24] Komjathy A L Sparks and A J Mannucci ldquoGenerating High

Precision Ionospheric Ground-Truth Measurementsrdquo US PatentNo 7289061 B2 filed 30 Oct 2007

[25] Datta-Barua S ldquoIonospheric Threats to the Integrity of Airborne GPSUsersrdquo PhD Dissertation Stanford Univ Palo Alto CA 2008

[26] Luo M Pullen S Dennis J Konno H Xie G Walter T Enge PDatta-Barua S and Dehel T ldquoLAAS Ionosphere Spatial GradientThreat Model and Impact of LGF and Airborne MonitoringrdquoProceedings of the 16th International TechnicalMeeting of the Satellite

Division of the Institute of Navigation IONGPSGNSS 2003 pp 2255ndash2274

[27] Luo M Pullen S Walter T and Enge P ldquoIonosphere SpatialGradient Threat for LAAS Mitigation and Tolerable Threat SpacerdquoProceedings of the 2004 National Technical Meeting of the Institute of

Navigation pp 490ndash501[28] National Geodetic Survey CORS Team ldquoCORS Continuously

Operating Reference Stationsrdquo httpwwwngsnoaagovCORS[retrieved 6 Jan 2009]

[29] National Geophysical Data Center ldquoSpace Physics Interactive DataResourcerdquo 2005 httpspidrngdcnoaagovspidr [retrieved1 March 2005]

[30] Sugiura M and Kamei T ldquoEquatorial Dst Index 1957ndash1986rdquoInternational Association of Geomagnetism and Aeronomy Vol 401991 pp 7ndash14

[31] Menvielle M and Berthelier A ldquoThe K-Derived Planetary IndicesDescription and Availabilityrdquo Reviews of Geophysics Vol 29 No 3

1150 DATTA-BARUA ETAL

1991 pp 415ndash432doi10102991RG00994

[32] Menvielle M and Berthelier A Correction to ldquoThe K-DerivedPlanetary Indices Description and Availabilityrdquo Reviews of

Geophysics Vol 30 No 1 1992 p 91doi10102992RG00461

[33] Poppe B B ldquoNew Scales Help Public Technicians Understand SpaceWeatherrdquo Transactions of the American Geophysical Union Vol 81No 29 18 July 2000 pp 322 328doi10102900EO00247

[34] Komjathy A ldquoAutomated Daily Processing of more than 1000Ground-Based GPS Receivers for Studying Intense IonosphericStormsrdquo Radio Science Vol 40 2005 pp RS6006doi1010292005RS003279

[35] Datta-Barua S ldquoIonosphere Threats to Space-Based AugmentationSystemDevelopmentrdquoProceedings of the 17th International TechnicalMeeting of the Satellite Division of the Institute of Navigation ION

GNSS 2004 2004 pp 1308ndash1317[36] Simili D V and Pervan B ldquoCode-Carrier DivergenceMonitoring for

the GPS Local Area Augmentation Systemrdquo Proceedings of IEEEIONPLANS 2006 San Diego CA 25ndash27 April 2006 pp 483ndash493

[37] LuoM Pullen S Datta-Barua S Zhang GWalter T and Enge PldquoLAAS Study of Slow-Moving Ionosphere Anomalies and TheirPotential Impactrdquo Proceedings of the 18th International Technical

Meeting of the Satellite Division of the Institute of Navigation ION

GNSS 2005 2005 pp 2337ndash2349[38] Lee J Luo M Pullen S Park Y S Enge P and Brenner M

ldquoPosition-Domain Geometry Screening to Maximize LAAS Avail-ability in the Presence of Ionosphere Anomaliesrdquo Proceedings of the19th International Technical Meeting of the Satellite Division of the

Institute of Navigation ION GNSS 2006 pp 393ndash408[39] Warburton J ldquoFAA HMI Analysis and Integrity Risk Compliance

Arguments (IRCAs)rdquo International GBASWorking Group (I-GWG) 8Meeting Agenda Item 62 Palermo Italy 3ndash6 March 2009

DATTA-BARUA ETAL 1151