ulich : long term trends in ionosonder data

8/8/2019 Ulich : Long Term Trends in Ionosonder Data

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Chasing trends in noisyChasing trends in noisyionospheric data: theionospheric data: the

SodankyläSodankylä IonosondeIonosondeThomasThomas UlichUlich ,,

MarkMark ClilverdClilverd , Martin Jarvis,, Martin Jarvis,andand HenryHenry RishbethRishbeth

SodankyläSodankylä Geophysical Observatory, FinlandGeophysical Observatory, FinlandBritish Antarctic Survey, UKBritish Antarctic Survey, UK

University of Southampton, UKUniversity of Southampton, UK



DoublingDoubling of [COof [CO 22 ] and [CH] and [CH 44 ]]

Mesosphere byMesosphere by 10 K10 K andandThermosphere byThermosphere by 50 K50 K ..

coolscools

Greenhouse CoolingGreenhouse Cooling600600

400400

200200

00

Layer of maximum electronLayer of maximum electrondensitydensity

by 15-20 km.by 15-20 km.

l o w e r s

l o w e r s Electron Temperature

Density



Helsinki

Oulu

SodankyläAn ionosonde is a vertical sounding radar developed in the 1920s.

The Sodankylä Ionosonde started on 1stAugust 1957.

It performed one sounding every 30 minuntil November 2005 (more duringcampaigns).

Since Nov ’05, 3rd generation ionosonde(α Wolf) every 10 min; IPY every minute!

High data quality: first 789.000 ionogramswere analysed by the very same person(45+ years).

SodankyläSodankylä IonosondeIonosonde



Some weirdempirical formulae.

ORTitheridge’s polynomial

method.

foEfoE

foF2foF2

M(3000)F2M(3000)F2



M(3000)F2 atM(3000)F2 at SodankyläSodankylä



foEfoE atat SodankyläSodankylä



foF2 atfoF2 at SodankyläSodankylä



Empirical hmF2 formulaeEmpirical hmF2 formulaebM ahmF !=2

6.1)35.2(

4.1355

1890

35.2 !!=

!!=

! xb

xa

176)(1490

2 !"!

#=

M M

F M hmF

012.0215.1

253.0 !!

=" x

M

12967.110196.0

)( 2

2

!

+="

M

M M F M

1761490

2 !=

M hpF

17614902 !"!

=

M M hmF

32

41 F

F x

F F M +

!

"=#

!!"

#$$%

& '((=

()=

)(=

+)=

1600exp

15014

)25(00064.03)0239.0exp(016.02.12

222.000232.01

2 R F

R F

R F

R F

Shimazaki [1955]

Dudeney [1974], eq. (56)

Bradley, Dudeney [1973], eq. (3)

Bilitza, Sheikh, Eyfrig [1979 ]

foE foF x /2=

2)3000( F M M =

TermCorrection=! M LatitudecGeomagneti=!

Number Sunspot= R



hmF2at

Sodankylä



hmF2 at SodankylähmF2 at Sodankylä



Multi-parameter fittingStochastic Inversion

Modelling of dataSubtraction of known

components



Known componentsKnown components

•• The temporal behaviour of a givenThe temporal behaviour of a givenionospheric time series contains aionospheric time series contains a

number of known components:number of known components: – – solar activity variations (11/22-year cycles)solar activity variations (11/22-year cycles) – – geomagnetic activitygeomagnetic activity

– – annual (seasonal) cycleannual (seasonal) cycle – – semi-annual cyclesemi-annual cycle – – (non-linear) combinations of the above(non-linear) combinations of the above



Unknown componentsUnknown components

•• The known components have to be removedThe known components have to be removedfrom the time series in order to reveal thefrom the time series in order to reveal theunknown components, which make up theunknown components, which make up thevariation of the residual:variation of the residual: – – changes of atmospheric chemical compositionchanges of atmospheric chemical composition

(greenhouse effect)(greenhouse effect) – – changes of Earthchanges of Earth ’’s magnetic fields magnetic field – – changes of changes of thermosphericthermospheric winds (dynamics)winds (dynamics) – – measurement errorsmeasurement errors – – something else? Something exciting???something else? Something exciting???



Making modelsMaking models•• Base functions of the model(s) are, e.g.:Base functions of the model(s) are, e.g.:

mm ii == εε ii ->-> measurement errorsmeasurement errors+ x+ x 11 ->-> constantconstant

+ x+ x 22 ttii ->-> sampling timessampling times+ x+ x 33 FF 10.710.7 (t(t ii)) ->-> solar activitysolar activity+ x+ x 44 Ap(tAp(t ii)) ->-> geomagnetic activitygeomagnetic activity+ x+ x 55 sin(2sin(2 ππ ttii))+ x+ x 66 cos(2cos(2 ππ ttii)) ->-> annual variationannual variation+ x+ x 77 sin(4sin(4 ππ ttii))+ x+ x 88 cos(4cos(4 ππ ttii)) ->-> semi-annual variationsemi-annual variation+ ...+ ...



The ionospheric property of interest is function of time anda number of other parameters. The model of the data istherefore

where

The actual measurements m i observed at time t i are equalto the model plus some measurement error ε i

Modelling the dataModelling the data



Measurements and theory are weighted by themeasurement errors:

The solution of the inverse problem is the vector x, whichminimises the following expression:

We are left with a general least squares problem. Solvingthis results in the most probable solution for x .

Inverse problem IIInverse problem II



The inverse problem can be solved by means of Singular Value Decomposition (SVD). Any matrix can be expressed asa product of two orthogonal matrices and a diagonal matrix:

Orthogonal means:

Therefore:

The errors of the unknowns are already contained in thissolution:

Errors of unknownsErrors of unknowns



SodankyläSodankylä hmF2 TrendhmF2 Trend



Ionospheric Greenhouse Effect?Ionospheric Greenhouse Effect?

•• Yes! Yes!



Ionospheric Greenhouse Effect?Ionospheric Greenhouse Effect?

•• Yes! Yes!•• ...at least in Sodankylä......at least in Sodankylä...



F2 Layer at AlmaF2 Layer at Alma AtaAta



hmF2 TrendshmF2 Trends



Problems IProblems I

•• Type of data used by various authors:Type of data used by various authors: – – hourlyhourly

– – 3-hourly (like geomagnetic indices)3-hourly (like geomagnetic indices) – – daily (selection of hours or averages of a certaindaily (selection of hours or averages of a certain

time of day like noon (10-14LT))time of day like noon (10-14LT)) – – monthly (unphysical, months are of differentmonthly (unphysical, months are of different

length)length) – – seasonal (averages of certain months)seasonal (averages of certain months) – – annualannual



Problems IIProblems II

•• Low-pass filtering:Low-pass filtering: – – often a running mean filter is employed tooften a running mean filter is employed to

reveal underlying overall behaviour reveal underlying overall behaviour – – some authors use polynomial fits insteadsome authors use polynomial fits instead•• How can we ensure that the filteredHow can we ensure that the filtered

data still represent the physicaldata still represent the physicalbehaviour of the observed property?behaviour of the observed property?

•• ““Smooth to deathSmooth to death ”” - one can make- one can makeeverything correlate.everything correlate.



E.g.: 11-year running meanE.g.: 11-year running mean



Problems IIIProblems III

•• How to remove solar activity?How to remove solar activity? – – SinusoidSinusoid – – Sunspot Number Sunspot Number – – Group Sunspot Number Group Sunspot Number – – F10.7 radio fluxesF10.7 radio fluxes

•• observedobserved•• adjusted to 1 A.U.adjusted to 1 A.U.

– – solar Lymansolar Lyman αα indexindex – – E10.7 proxy from Solar2000E10.7 proxy from Solar2000 – – lower resolution than ionospheric data, i.e.lower resolution than ionospheric data, i.e.

smoothed versionssmoothed versions



Problems IVProblems IV

•• What about geomagnetic activity?What about geomagnetic activity? – – Which index?Which index? aaaa ,, akak ,, AkAk,, apap ,, ApAp, K,, K, KpKp??

– – Which representation? 3-hourly?Which representation? 3-hourly?Smoothed?Smoothed?

– – Some argue that including geomagneticSome argue that including geomagnetic

activity only leads to increased noise.activity only leads to increased noise.



Problems VProblems V

•• Which method should be used for trendWhich method should be used for trenddetermination?determination?

– – multi-step removal of known componentsmulti-step removal of known components – – single-step removal by multi-parameter fittingsingle-step removal by multi-parameter fitting

•• The error propagation through the multi-stepThe error propagation through the multi-stepmethod is very difficult to estimate.method is very difficult to estimate.

•• What to do with gaps in the data? This isWhat to do with gaps in the data? This iscrucial for historic geophysical data sets.crucial for historic geophysical data sets.



Problems VIProblems VI

•• What are the errorsWhat are the errors ε ε i i of theof themeasurements?measurements?

– – accuracy of accuracy of foEfoE 0.05 MHz???0.05 MHz??? – – accuracy of foF2accuracy of foF2 0.1 MHz???0.1 MHz??? – – accuracy of M(3000)F2accuracy of M(3000)F2 0.05???0.05???

•• ... but these are only scaling... but these are only scalingrequirements, errors are likely to berequirements, errors are likely to belarger!larger!



Problems VIIProblems VII

•• The height of the F2-layer peak (hmF2)The height of the F2-layer peak (hmF2)is (usually) not routinely scaled.is (usually) not routinely scaled.

•• It can be estimated empirically.It can be estimated empirically.•• Several methods have been derived inSeveral methods have been derived in

the past.the past.

•• Which method should be used?Which method should be used?•• The method depends on the ionosondeThe method depends on the ionosondelocation.location.



Problems VIIIProblems VIII

•• Without removal of cyclic components,Without removal of cyclic components,the observed trend depends upon thethe observed trend depends upon the

phase of the cyclic components in thephase of the cyclic components in thedata.data.•• Removal helps, but due to noise, theRemoval helps, but due to noise, the

cyclic components cannot be removedcyclic components cannot be removedentirely.entirely.

•• Even after reducing the data, the trendEven after reducing the data, the trendmight still depend upon the phase.might still depend upon the phase.



Damped oscillator Damped oscillator ““ringingringing ””

True trend is zero. Note that trendnever becomes positive, i.e. itdoes not oscillate around thecorrect value.



RingingRinging

The ringing idea was first introduced by Jarvis etal., 2002. The plots shown here are from afollow-up paper by Clilverd et al., 2003.



Global hmF2 TrendsGlobal hmF2 Trends



Is this hopeless?Is this hopeless?

•• I donI don ’’t think so.t think so.



Is this hopeless?Is this hopeless?

•• I donI don ’’t think so -- yet.t think so -- yet.



Trends in other ObservationsTrends in other ObservationsHeight Method Parameter Trend Referencein km per Year

Thermosphere OH & O emissions Temperature -3 K Semenov, 1996

Thermosphere Red O emission Layer height -1 km Semenov, 199697 Green O emission Temperature none Golitsyn et al., 1996

92 Lidar Na layer height -47 m Clemesha et al., 199292 Lidar Na layer height -39 m Clemesha et al., 1997

87 OH emissions Temperature -0.7 K Golitsyn et al., 199682 NLC Temperature -0.28 K Gadsden, 1990, 1998

80 Radiowave reflection Pressure +0.4% Taubenheim et al., 199075 Sounding rocket Temperature -0.6 K Kokin and Lysenko, 1994

70 Sounding rocket Temperature -0.7 K Golitsyn et al., 199660-70 Lidar Temperature -0.4 K Hauchecorne et al., 1991

60 Sounding rocket Temperature -0.4 K Golitsyn et al., 199660 Sounding rocket Temperature -0.33 K Keckhut et al., 1999

50-60 Lidar Temperature -0.25 K Aikin et al., 199150 Sounding rocket Temperature -0.25 K Golitsyn et al., 1996

40 Sounding rocket Temperature -0.1 K Golitsyn et al., 199630-60 Sounding rocket Temperature -0.17 K Dunkerton et al., 1998

30-50 Sounding rocket Temperature -0.17 K Keckhut et al., 199930 Sounding rocket Temperature -0.1 K Golitsyn et al., 1996

25 Sounding rocket Temperature -0.1 K Golitsyn et al., 1996

25 Sounding rocket Temperature -0.11 K Keckhut et al., 1999

CENSORED



ConclusionsConclusions

•• The ionosphere is changing on long timeThe ionosphere is changing on long timescales!scales!

•• But we do not yet understand this change.But we do not yet understand this change.•• Ionosondes perform regular measurements of Ionosondes perform regular measurements of

the near-Earth space.the near-Earth space.•• Ionosondes have done this since the 1930s.Ionosondes have done this since the 1930s.•• Long time series of observations of our Long time series of observations of our

environment should not be discontinued!environment should not be discontinued!



Thank you!Thank you!

ulich : long term trends in ionosonder data

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