june 1982 the aircraft dropwindsonde system in the global

June 1982

An occasional series reporting on U.S. and international GARP scientific, technical, and planning activities, developments, and programs, presented as a public service to the meteorological com-munity by the American Meteorological Society through arrangements with the U.S. Committee on the Global Atmospheric Research Program of the National Academy of Sciences-National Research Council. Opinions expressed in "GARP Topics" do not necessarily reflect the point of view of the U.S. Committee.

The Aircraft Dropwindsonde System in the Global Weather Experiment

Paul R. Julian

National Center for Atmospheric Research, Boulder, Colo. 80307

Abstract

A major component of the Tropical Observing System deployed dur-ing the Global Weather Experiment was the Aircraft Dropwind-sonde System. The emphasis in this article is upon the processing of the data gathered by this system and upon factors that influence the quality of the data. The wind-finding algorithm is presented together with a description of the interactive raw data editing procedures. Some examples of drops illustrating various points are included.

1. Introduction

A major component of the Tropical Observing System of the Global Weather Experiment (or F G G E ) involved: a rela-tively new system for finding winds; a dozen aircraft; and hundreds of individuals. It was the single most expensive special observing system in the GWE. Because of the impor-tance of the Aircraft Dropwindsonde System (ACDWS) in making measurements in the tropics and because users of the data produced should have a basic understanding of this fairly complicated system, I have attempted to outline those topics that deserve attention by the scientific community.

0003-0007/82/060619-09$06.25 © 1982 American Meteorological Society

Bulletin American Meteorological Society

Some of the material included can be found in various GARP documentation. However, some has not been generally available and it all deserves aggregation in a single contribution.

The G W E Dropwindsonde System has been described by Scribner and Smalley (1981). On the assumption that the data user is more concerned with the data processing and quality than with the system itself, no detailed description of the system will be included here.

In various sections of this contribution I outline the proc-essing of the Level II—b data, include a brief mathematical review of the algorithm used to calculate the sonde winds, and provide some samples of the effects of the more impor-tant variables influencing wind vector accuracy. Also, I in-clude some comparisons of simultaneous wind vector deter-mination using both the Omega-based algorithm and radar. Finally, I include some comments on the procedures used to provide the on-board, real-time sounding data now archived as Level I l -a or Monex Quick-look data.

2. Brief description of observing system

Dropwindsonde data were obtained by aircraft operating out of staging bases in Hawaii, Panama (SOP-1), Mexico

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(SOP-2), Ascension Island, and Diego Garcia. Instrument packages containing Omega Navigational-Air Signal Re-ceivers/Retransmitters, as well as pressure, temperature, and humidity sensors were deployed on parachutes from aircraft flying as high as possible consistent with maintaining maxi-mum cruising range. As the instrument package descended at a nominal rate of 25 mb min-1 (average of a little over 300 m min-1), the sonde received the 13.6 kHz Omega signals and retransmitted them to the aircraft, along with signals derived from the sensors. The movement of the sonde, and thus the wind vector, is derived by calculating the rate of change of position in the Omega navigational net. Onboard the air-craft, these data were recorded and processed into Part A of the standard TEMP DROP code (wind, temperature, pres-sure, humidity at standard levels) and transmitted in near real time to ground stations for relay to the Automated Weather Network and Global Telecommunication System (Level Il-a data). All recorded data were processed post-flight at the Aircraft Dropwindsonde Data Center at NCAR for inclusion in the delayed data set (Level II—b data).

3. Products provided for FGGE research

A total of 5013 soundings have been sent from NCAR to the Level I l -b Space-Based and Special Observing System Data Center (SBSOSDC) for incorporation into the Main Level I l -b data set. These cover the periods from 15 January-26 February 1979; 10 May-9 June 1979; and the MONEX periods. For IOP-1, a total of 2154 drops survived the inter-active editing process (next section) and for IOP-2, 2224 drops were sent to the Swedish Level II—b Data Center. For the December 1978 Winter MONEX period, 177 drops were included in the data set, and for the Summer MONEX, 458 drops were sent.

Because an unanticipated problem with some of the Omega signals was not discovered until after the Main Level II—b had been archived, the entire ACDWS data set has been reprocessed for the Final II—b data set. This problem is dis-cussed in Section 7.

In summary, the user should be aware of the different FGGE data sets and the characteristics of each. Briefly, these are for the ACDWS data:

steps. The first step was a preprocessing accomplished on the NCAR ACDWS Interactive Computer Graphics System, and the second was the final computations and data format-ting carried out on the NCAR CDC 7600 system. The NCAR ACDWS interaction system consisted of: a) tape cassette reader; b) 7- and 9-channel tape drives; c) disk-pack memory unit; d) CRT display unit and control unit; and e) PDP11-03 computer with 30 X 103 memory.

The interactive system was used to record data from the cassettes, reformat data into basic units (such as centicycles for Omega phase signals and frequency for the sensor teleme-try), and display these for editing purposes. The data treated then went onto the disk file or onto reel magnetic tape for processing on the CDC 7600. The interactive capability was particularly useful because the data from each drop were dis-played on a CRT terminal scope and scrutinized. The opera-tor eliminated obvious noise spikes, denoted lane changes in the Omega phase records, specified drop termination times, and evaluated the disposition of each of the seven possible Omega records. When a full reel of edited and refiled drop data had been logged to magnetic tape, the reel was submit-ted to the CDC 7600 system. The software reduced sensor frequency data to the physical units, calculated winds from the Omega phase data, determined standard and significant levels, determined quality indicator information, and ar-chived the resulting sounding on the NCAR Mass Storage System. When a sufficient number of soundings had been completely processed, a final formatted tape intended for the Swedish Level II—b Data Center was written.

The pressure, temperature, and relative humidity meas-ured by the sonde also were edited by the operator. In gen-eral, the editing philosophy was much the same as with the Omega signal data. However, if none of the thermodynamic variables was usable, the drop was not discarded, and pro-vided that usable Omega data were available, the final proc-essing code reconstructed the pressure profile from the drop splash-down times using a standard fall rate of the sonde. The final processing contained gross climatological checks on the pressure and temperature. It should be noted that owing to the uncertainty of measurement of sea-level pres-sure and the lack of a FGGE requirement, geopotential height data were not computed and included in the Level I l-b data.

Level Source Il-a Global Telecom-

munications System (GTS)

MONEX on-board aircraft quick look

Main I l -b NCAR/SBSOSDC

Final I l -b NCAR/SBSOSDC

Regional N C A R / M O N E X Centers

Characteristics Number Synoptic real-time, 3 2599

station omega. No quality figures

Synoptic real-time, 3 ? station omega. No quality figures

Post-processed, includes problem soundings.

All known errors corrected. Includes MONEX

soundings

All known errors corrected.

4378

5013

209 Wi 520 Su

4. Data processing

The processing of the primary cassette data took place in two

5. Wind finding

Extensive literature on the theory and practice of using VLF (Omega) navigation signals for tracking sondes, and thus providing wind soundings, has appeared. I will follow closely the development by Passi (1974, 1977) both in notation and in spirit.

eh\,k

ehi,k

nh\,k nh2,k

ehk-i,k nhk-u

P\,k Pl,k

Pk-\,k

+

0\,k

02,k

Ok-l,k

in which the pjk are the measured and smoothed Omega phase rate differences, the hjk the elements of the hyperbolic


Bulletin American Meteorological Society 621

gradients (east and north components), and the oy* the error matrix of the phase-rate differences obtained from the smoothing process on the phases of the received Omega sig-nals. In general, with k Omega stations, the matrix system contains k-1 rows derived from the independent combina-tion of the stations. Thus, with k > 3 the system of equations is overdetermined and a weighted least-squares method of solution is employed. The solution

= ( H r i r , H ) - 1 H r i r 1 t (2)

is the "conventional" overdetermined solution for problems cast in this general form.

The elements of the error matrix L should, in fact, be the estimates of the observed phase-rate errors. Passi has argued, on justifiable grounds, that these errors may be estimated from the smoothing or filtering process on the individual Omega phase data, so that, apart from an arbitrary constant, C,

where

and

2 = Cs\\f

ilifj = <A,2 + i f / = 7

ijfl if i ^ k . !((/) * k

= {pj ~ Pj}2

where p j is the smoothed or filtered value of Omega station j's phase at time t.

The methods of smoothing the Omega phases to remove noise and produce reliable phase rates have been discussed by Govind (1975), and Acheson (1974). Although the partic-ular method of signal smoothing used in processing the GWE dropwindsonde data has not been presented or used previously, the method is not conceptually different from those discussed in the literature in that the purpose is to elimi-nate noise and obtain reliable estimates of the phase rates.

The entire discrete Omega phase data for each station used, adjusted for lane changes, are transformed by a fast Fourier transform into frequency (co) space. The resulting complex discreet transform is then multiplied by three quan-tities, (1) a frequency dependent transfer function F(a>) rep-resenting the low-pass filter response desired, (2) a complex exponential exp(—/<wr; ) where rj represents the time interval between the particular Omega station's assigned transmis-sion time and the common fiducial time to which all stations are to be referred, and (3) the quantity icj to produce the phase rate as the derivative of the phase data. Thus, the smoothed and adjusted phase rates for station j are

Pj= E exp(i«0 I ( 7" E p j ( 0 co U i v r

X exp(- F(oj) exp(—icurj OX^)]

The particular low-pass filter used is specified by a transfer or filter function having a half-cosine response between the roll-off frequency, a>i, and the cut-off frequency, m. Experimen-

FIG. 1. A plot of the Hawaii 13.6 mHz Omega signal received from a test dropwindsonde. Curve A is the raw recorded Omega phase, in centicycles, but corrected for lane changes (one lane = 100 centicycles). Curve B is the low-pass filtered curve which approxi-mates the noise-free signal. Curve C, below, is the smoothed residual or difference in Curves A and B.

tation with test drop data, using radar winds as "truth," led to the selection of o>i = (0.002)27rand m = (0.004)2tt hertz. The discrete weights corresponding to this transfer function show very small side lobes outside of the principal weights— the effective length of the filter is about four minutes.

a. Wind quality assessment

Returning to Eq. (2), we note that the larger the (relative) high frequency variance, or noise, on a given Omega channel, the lower that station's influence (through the appropriate p) will be on the solution vector. It is most important, how-ever, also to note that the trace of the matrix (H r\|/~1H)~1 rep-resents, to within an arbitrary constant, the "error" or uncer-tainty in the solution vector. In the standard statistical linear model, this quantity is the variance-covariance matrix of the estimated solution vector, and the translation to the interpre-tation in the present case seems straightforward. Moreover, in the case when k = 3 and the system reduces simply to two equations in two unknowns, the quantity (H r\|/_1H)_1 still ex-ists and is easily calculated even though it does not affect the solution vector. This may seem a bit odd but, in this instance, it is necessary to recall that the phase rates are still specified as containing error. A geometrical interpretation of this situation is that the two values of the phase rate differences determine w, v, and that the appropriate \\r elements interact-ing with the elements of H specify an uncertainty in i/, v space.

The importance of this quantity in assessing the quality of the winds measured by the system will be underscored in a later section.

Figure 1 gives an illustration of the filtering applied to the


622 Vol. 63, No. 6, June 1982

phase data. The phase signal shown, in centicycles with the lane changes removed, was the Hawaii 13.6 kHz signal re-ceived and retransmitted by a sonde dropped near Patrick Air Force Base, Fla. The smoother curve, of course, is the low-pass filtered result. It is important to remember that both sets of data are discrete; the filtered curve has been plot-ted as continuous to emphasize that the combined time-shift and differentiation produced by the operation in frequency space result in the phase rate at a point shifted in time (by rj) along the filtered curve. The curve is intended to represent what the receipt of Hawaii's signal would have been in the hypothetical situation of continuous transmission and noise-free reception.

The bottom portion of Fig. 1 shows a smoothed residual or high-pass variance. In this instance, the aircraft made a number of turns while the sonde descended—these turns should be obvious from the large phase changes exhibited as the aircraft moved through the Hawaii's signal lanes. It is in-structive to note that the residual, or error-variance, in-creases at each of the turn points. These higher error varian-ces mean a more uncertain estimate of the true phase-rate. This example was taken during the testing of the FGGE dropwindsonde system on the NCAR Electra. None of the FGGE aircraft, however, performed maneuvers as complex as that shown here.

b. Quality control— Wind profiles

Since the primary exercise of quality control on the wind vec-tors was carried out in the interactive editing procedure, some description of the editing and its subjective or operator-oriented aspects is in order. After checking the digital data on time, position, etc., with the crew's logs, the operator caused the time sequence of each Omega station's phase data to be displayed on the CRT scope. A general impression of the quality was immediately obvious—gaps, noise spikes or bursts, and unresolved Omega lane jumps were all detecta-ble. In addition to correcting lane changes and eliminating single noise spikes, the operator edited noise bursts, or the sequence of Omega phase measurements that were not repre-sentative of aircraft and sonde movement. It might be ex-pected that no distinct boundary was evident between these edited noise bursts and the unedited background, and indeed there was not. Experience gained by editing and re-editing a number of times drops from the first few weeks of IOP-1, provided a semi-objective basis for this editing.

If, in the operator's judgment, at least three Omega sta-tions with reasonably noise-free signals and adequate geome-try were unobtainable, the drop was discarded and no further processing occurred. The operator's judgment here was based, indirectly, upon objective uncertainty or error infor-mation resulting from the Omega station geometry and sig-nal-to-noise ratios. It is pointed out that this was based indi-rectly, because the quantitative information specific to the drop being edited was not available at the time of the editing. However, experimentation with the final wind calculations using the edited Omega signal data of varying quality and combinations quickly made it evident at what levels the sub-jective judgments must be set. In other words, if the Omega signal quality displayed on the scope was such that the final

wind vectors were to be uncertain to, say, 15 ms"1 or more, that Omega sequence would be of no utility and therefore would be discarded.

In terms of Level II—b quality control, each wind vector was appended with the quantitative uncertainty information calculated in the final processing on the CDC 7600, as out-lined in the FGGE Data Management Plan. It must be em-phasized that the uncertainty thus quantified is the result of the geometry and signal quality only. It does not include sources of error resulting from ionospheric disturbances or other Omega propagation characteristics which would influ-ence the Omega phase rates, and which are external to the airborne system itself (Section 7).

6. Thermodynamic data

The temperature and humidity sensors and their exposure on the NCAR-designed dropsonde are described by Scribner and Smalley (1981). The redesign from earlier versions was to correct inertia in the sensing elements, and to change the modulation scheme. Sonde baselining in FGGE was ac-complished by inserting standard resistors in the tempera-ture and humidity sensor clips and recording the transmitted frequencies of those standard resistors. For pressure, the cabin pressure measured by the sonde was compared with the same pressure measured by means of a calibrated pressure aneroid in the on-board system. The evaluation of the sonde pressure measurements will be discussed first, because their accuracy affects the evaluation and comparison of tempera-ture and humidity data (because of the meteorological use of pressure as the independent variable).

Subjective examination of tropical drops made between 5°S and 15°N, allowed one problem to be detected very early. Normally, sonde impact with the ocean surface is detected by the operators or by examination of the Omega or sensor tele-metry. Thus, specification of impact time (or pressure) can be made, but might be uncertain because of signal quality or the inherent timing of the telemetry to something like ± 5 s. At the surface, the sonde is falling at about 25 mb min-1 so that there is an uncertainty from this fact alone of about 2 mb in the "splash" or sea-level pressure. Since this is about the normal climatological variability of sea-level pressure at a location in the tropics, we may simply compare the sonde splash pressures with climatological pressures appropriate to the sonding location. Such comparisons clearly show a bias toward low "splash" pressures. On drops with clean signal characteristics, so that the splash "times" are known to within "2 mb," this bias appears to be larger than —2 mb. An estimate from a sample of about 60 drops gives a value of about —7 mb. At present, no explanation for this bias has presented itself. The NOAA FGGE Project Office checked the pressure standards in the on-board system with precision aneroids after each IOP and no systematic offsets were found. A time lag in the adjustment of the pressure standard to changes in cabin pressure might be a possible source of the problem (Smalley, private communication).

Subjective examination of the temperature and humidity profiles indicates good detail and features, e.g., stable layers, which are consistent on adjacent drops. Examples will be



shown in a later section. Comparison of drops made near conventional rawinsonde stations indicates no differences greater than those accountable from considerations of the re-spective sensor error variances. The humidity sensor, in par-ticular, seems to be capable of recovering after passing through saturated layers.

7. Basic considerations of the accuracy of Omega-derived winds

The commonly posed question, "How accurate are winds de-rived from the ACDWS?", does not have a simple answer. From the foregoing discussion on wind-finding using Omega signals, it should be apparent that many variables can affect the accuracy of the derived winds.

However, to provide a basis for discussion, I will first pre-sent some results of wind measurements made by Omega dropwindsondes that were tracked by ground-based (FPS-16) radar. These comparisons are the most meaningful because of the difficulties inherent in attempting to define accuracy. Both the radar-derived and Omega-derived winds contain error, of course, but the relative error shown is more easily interpretable in the context of uncertainty in meteorological analysis of wind fields. Figure 2 shows a comparison of radar-track winds (points) and Omega winds (continuous curve) for a test drop made in April 1980 off the east Florida coast. The graphical presentation compares the zonal and merid-ional components separately on a geometric height scale. The smoothing performed on the radar data was purposely selected to be over 30 s, providing a vertical resolution about eight times smaller than that of the Omega winds. Compari-son of the wind components shows the expected result: The Omega winds track the radar winds quite well, with the higher resolution radar winds showing detail that the more heavily smoothed Omega winds cannot match. A quantita-tive check made on the wind components after the radar winds have been smoothed to an equivalent resolution indi-cate a mean zonal component difference of 1.7 m s_1 and mer-idional component differences of 1.3 m s-1. The largest mag-nitudes are 3.7 and 2.9 m s-1, respectively.

In these test drops the relative location of the sonde and the Omega transmitters was near optimum and the Omega phase signal qualities were excellent. Six Omega stations were used in calculating the winds shown. For the routes flown in the Global Experiment, such optimum conditions were not always present; some further explanation of various important features is needed.

To illustrate a number of these points, a sample drop has been processed by using first three, then an increasing number of Omega signals. Figure 3 shows a map projection illustrating the geometry of this situation. Great circle lines have been plotted from the drop point to seven Omega sta-tions (only antipodal Liberia has been omitted). Note that for this drop Hawaii and North Dakota are almost exactly on the same azimuth and therefore are redundant.

The middle panel of Fig. 4 shows the wind profile when five stations (Japan, Norway, Hawaii, North Dakota, and Argentina) are used. The error variances for all stations but Argentina are good; that station's signal has two to three

FIG. 2. A comparison of the zonal (left) and meridional (right) wind components from a radar-tracked test dropwindsonde, Patrick Air Force Base, Fla., 18 April 1980. The continuous curves are the Omega-derived winds and the points are the radar-track winds. The abcissa is in meters per second and the ordinate (height) is in meters.

times the error variance of the other signals. The quantitative wind vector uncertainties for this case vary from 4.3 m s"1 (at 790 mb) to 1.4 m s"1 (at 400 mb). The right-most panel shows the winds for the same drop using only Japan, Norway, and Hawaii. These three stations have the best signal quality and were the three stations selected by the on-board operators. This wind profile differs significantly from the first one, and it is important to note that the three-station geometry is mar-ginally poor. The wind vector uncertainties are now larger, 5 to 9 m s-1, reflecting, in spite of the better overall signal qual-

FIG. 3. An equal-areal projection centered on the location of the drop shown in Fig. 4. Great circle arcs to each of the Omega trans-mitters are shown to illustrate the sonde-transmitter geometry. The ellipse depicts the solar terminator of the time of the drop.


624 Vol. 63, No. 6, June 1982

FIG . 4. Dropwindsonde sounding made 2 February 1979. The ordinate is a linear pressure scale, the left panel depicts the temperature and dew point data, and the middle and right panels depict two wind profiles determined using different sets of Omega phase-data (see text). The wind directions, D (dashed line), refer to the scale at the top and the wind speeds; S, to the scale at the bottom of the panels.

ity, the poorer geometry. The average vector difference be-tween the two profiles is 3.5 m s"1, with winds at some levels 6 to 7 m s"1 apart.

We interpret this result to indicate that in the case of excel-lent signal quality and optimum geometry, the exact smooth-ing procedure used is not critical. Moreover, it illustrates the fact that the geometry of the sonde and Omega station array is, for usable signal qualities, more important than the rela-tive signal quality of the Omega stations utilized. In this case, the crucial factor was the addition of the Argentine Omega station, even with its relatively poor signal quality. (With poor geometry, however, the particular smoothing proce-dure used apparently does become important—the winds calculated by the onboard system and by the GWE post-proc-essing algorithm (using exactly the same three-station raw

Omega data) differ by a significant amount. Note, however, that the quantitative vector errors calculated by the post-processing algorithm provide a warning of the uncertainties of the situation. No such warning or indication was available from the on-board algorithm.)

One major problem inherent in Omega wind determina-tion unfortunately was not fully appreciated until after the processing of the Main Level II—b data was completed and sent to the SBSOSDC. In some instances, Omega signals from the lower latitude Omega stations (e.g., Hawaii, Liberia) demonstrated an anomalous propagation in that the east-ward propagating signal was much stronger than the west-ward signal. Thus, at great circle distances of approximately 100-180 arc degrees to the west of the Omega station, the sonde was effectively receiving the signal propagating east-

TABLE 1. ACDWS tests, Patrick Air Force Base, Fla., April 1979. Sonde 02510, 15 April

Djr/speed Dir/speed Dir/speed z P 7 station H w/o LaR QF* radar

(m) (mb) (deg./m s"1) (deg./m s"1) (m s'1) (deg./m s'1)

5740 500 228/22.5 233/42.4 0.5 231/40.0 4350 600 230/20.1 231/34.6 1.2 232/36.5 3100 700 235/20.3 238/32.9 1.8 240/29.6 2043 800 280/17.2 270/20.5 0.7 270/23.0 1840 850 297/15.6 288/17.3 0.6 288/18.2 1052 900 300/9.7 289/11.0 0.6 289/11.2 600 950 v 321/7.9 308/8.5 0.6 ?

*QF, vector uncertainty, m s !.



FIG. 5. Results of a drop during IOP-1, 18 January 1979 in the central Pacific. The abcissas and ordinate are the same as Fig.

ward at arc distances greater than 180° instead of that propa-gating the great circle minimum distance. Since the appar-ent sonde-transmitter geometry was reversed in these instances, the computed winds were incorrect. A survey of the SOP-1 wind data suggests that about 20-30% of the drops in the Main Level II—b data set contain errors greater than 2 m s~\ with the percentages for some regions (e.g., the Arabian Sea) being relatively greater, and for others (e.g., the Atlantic) being less.

An appreciation of the effect of this anomalous Omega propagation on a wind profile is illustrated by reference to the test drop described above and portrayed in Fig. 2. As noted, six Omega stations were used in calculating this pro-file. A seventh station, LaReunion, was available and had ex-cellent signal-to-noise qualities. However, had LaReunion's signal been used, the wind vectors given in column 1 of Table 1 would have resulted. The characteristics of the con-taminated wind vectors determined by utilizing an anoma-lously propagating signal, viz., directions very close to, but speeds much less than the "correct" vectors, is typical of the errors in the Main Level II—b data caused by this problem.

Because of this error source, all FGGE dropwindsonde data were completely reprocessed and incorporated into the Final Level II—b data set (Section 3).

8. Examples

Some examples are shown here thai illustrate the points just discussed. Figures 5 and 6 present the temperature and dew point profiles (left) and wind direction and speed (right) on a linear pressure scale (ordinate). A linear pressure scale is used because the fall speed of the sonde is approximately lin-ear in pressure and the averaging internal used in smoothing

FIG. 6. Same as Fig. 5, but for a drop made 40 min later and 340 km from the drop shown there.

the phase rates is approximately equivalent to a four-minute or 100 mb interval.

Figures 5 and 6 show two drops approximately 3.5° apart in the deep tropics. Both soundings used three-station geometry, which was nearly optimum, with excellent quality Omega signals. Meteorologically significant features are: a saturated cloud layer extending to about 850 mb capped by a subsidence inversion, a middle cloud layer at about 500-450 mb, low-level southeasterly trades, and strong upper-level southwesterlies. The average uncertainty in the wind vectors, on both drops, was 2 m s_1 or less. The average wind vector difference between the winds shown and those computed on board the aircraft (FGGE Il-a data) was 1.1 m s"1 in one case, and 0.8 m s"1 in the other. The consistency of these fea-tures on the two drops is reassuring and is a reflection of the system's ability to produce data meeting the requirements of the GWE.

Figure 7 is a composite intended to illustrate the synoptic-scale impact of the ACDWS data. Two charts at constant pressure levels (850 and 300 mb) are shown for the eastern Pacific tropics at 1800 GMT, 1 February 1979. The wind vec-tors and temperatures for nine drops are depicted in conven-tional fashion together with those from conventional data, cloud motion vectors, and Tropical Observing Ship (TOS) soundings. (The remaining drops from this 1 February sortie occurred after 2100 GMT and were included in the subse-quent analysis.) Also shown is a portion of the infrared imag-ery from a DMSP satellite pass at 1300 GMT. Superimposed on the charts are the wind field analyses, in the form of ar-rows and isotachs, of the ECMWF Level III—b analysis-fore-cast scheme (Lorenc, 1981). Although there is much of inter-est in this figure, I would point out the following: There is definite indication of low-level convergence and upper tropo-spheric divergence associated with the portion of the ITZ straddled by the aircraft track. The ITZ, as depicted by the


41 Vol. 63, No. 6, June 1982

FIG. 7. Charts at the 850 mb (top) and 300 mb (center) levels for 1800 GMT, February 1979, in the eastern tropical Pacific. The analy-zed wind field is depicted by arrows and iso-tachs (m s_1) and the Level II—b data used are shown in conventional fashion. The data from nine dropwindsondes are shown: these are or-iented approximately along 1°S and 6°N and are identified by the letters DS at the base of the wind vector. Cloud-motion vectors are identified by other two letter combinations (e.g., CM) and data from the Tropical Observ-ing Ships by five-letter combinations (e.g., JORDA). The bottom panel to the same scale is the infrared imagery obtained from a DMSP satellite pass at 1300 GMT.



infrared imagery, does not seem to be particularly intense or well organized in this example, but moderate convection along 5°N must be rather closely associated with the conver-gence/divergence indicated by the dropsonde winds. The wind vector shown at 5°N, 91°W (labeled JORDA) on each chart are from the Omega shipboard system aboard the RV D.S. Jordan. They agree in excellent fashion with the ACDWS vectors. The two western-most dropwindsonde vectors, at 110°W, indicate relatively strong 300 mb west winds, which are not apparently consistent with divergence out of the ITZ region. The ECMWF analysis scheme has not accommodated these wind speeds for reasons that could be in-vestigated, but are not germane to the present article. Exami-nation in detail of these two complete soundings does not re-veal any identifiable problem with the data: the vector uncertainties are on the order of 2-3 m s-1. Thus, although there may be some unidentified problem with the wind de-termination, they cannot be discounted out-of-hand. We are thus in the rather classical dilemma of synoptic analysis in attempting to reconcile data and analysis.

This example has been chosen more or less at random. It does provide a sample of the horizontal and vertical consis-tency of the ACDWS data, and how these data agree with other TOS data and a significant synoptic feature. For further analysis of the TOS data and their consistency, the reader is referred to Julian (198la,b).

Acknowledgments. So many people were involved in the drop-windsonde program that it would be impossible to acknowledge them all. However, in just the data processing aspects, I want to ex-tend my appreciation here to the following people who were instru-mental in producing the ACDWS data in its various forms: Justin Smalley, Herb Poppe, Dennis Shea, and Ranjit Passi of NCAR; Don Acheson of NO A A; Antti Lange of the Finnish Meteorological In-stitute; and Tom Johnson of TRACOR, Inc.

Appendix.—Acronyms

ACDWS Aircraft Dropwindsonde System DMSP Defense Meteorological Satellite Program ECMWF European Centre for Medium-Range Weather

Forecasts FGGE First GARP Global Experiment GATE GARP Atlantic Tropical Experiment GTS Global Telecommunications System IOP Intensive Observing Period ITZ Inter-tropical Convergence Zone NCAR National Center for Atmospheric Research SBSOSDC Spaced-based Special Observing System Data

Center, Norrkoping, Sweden TOS Tropical Observing System VLF Very low frequency

9. Summary

I have intended to include as much material in this article as will enable a user of the FGGE data to appreciate the charac-teristics and problems of the aircraft dropwindsonde data. These data, as well as any of the data from the special observ-ing systems deployed during FGGE, are the result of expen-sive and unconventional observing systems, and maximum benefit will be derived from them only if the data users and producer exchange information. Indeed, it was just such an exchange that led to the identification of the Omega propa-gation problem described in Section 7.

A great deal of information of a more technical nature concerning Omega wind finding could have been included here, but would doubtless have been more than the reader would have wanted. The writer would appreciate feedback from users of the ACDWS data. Some specialized use of the data, other than the use in Level III—b analyses for numerical prediction, has already begun, and I hope that more will take place.

References

Acheson, D. T., 1974: Omega windfinding and GATE. Bull Am. Meteorol. Soc., 55, 385-398.

Govind, P. K., 1975: Omega windfinding systems. J. Appl. Meteorol., 14, 1503-1511.

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june 1982 the aircraft dropwindsonde system in the global

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