DTIC ADA462183: A Statistical Approach to WindSat Ocean Surface Wind Vector Retrieval

0.17 MB·English

by  Defense Technical Information Center

164 IEEEGEOSCIENCEANDREMOTESENSINGLETTERS,VOL.3,NO.1,JANUARY2006
A Statistical Approach to WindSat Ocean
Surface Wind Vector Retrieval
CraigK.Smith,Member,IEEE,MichaelBettenhausen,Member,IEEE,andPeterW.Gaiser,SeniorMember,IEEE
Abstract—WindSat is the first space-based polarimetric mi- TABLE I
crowave radiometer. It is designed to evaluate the capability of NOMINAL NEDT VALUES FOR RAIN-FREE OCEAN
polarimetric microwave radiometry to measure ocean surface RETRIEVALCELLS(INKELVIN)
wind vectors from space. The sensor provides risk reduction for
the National Polar-orbiting Operational Environmental Satellite
System Conical Scanning Microwave Imager/Sounder, which
is planned to provide wind vector data operationally starting
in 2010. The channel set also enables retrieval of sea surface
temperature, and columnar water vapor and cloud liquid water
(W) referenced to 10-m height, compass wind direction ,
overtheoceans.Wedescribestatisticalalgorithmsforretrievalof
theseparameters,andacombinedstatistical/maximum-likelihood columnarwatervapor(V),andcloudliquidwater(L).Thepur-
estimator algorithm for retrieval of wind vectors. We present a poseofthiseffortisthreefold:1)obtainaquickinitialassess-
quantitative analysis of the initial wind vector retrievals relative ment of WindSat’s wind vector measurement capabilities; 2)
toQuikSCATwindvectors.
provide a retrieval-oriented test platform for enhancements to
Index Terms—Microwave radiometry, polarimetry, retrievals, theWindSatTemperatureandSensorDataRecordalgorithms;
wind. and 3) provide either an independent set of retrievals from, or
aprioridata for,a physically basedalgorithm.Theregression
techniqueandtheselectionofregressiontrainingdatawerede-
I. INTRODUCTION
signedtofacilitateretrievalofallfiveparameters,asdiscussed
WINDSAT is a conically scanning polar-orbiting multi-
inSectionsIIandV.However,inthisletterwefocusonappli-
frequency microwave radiometer launched in January
cationoftheregressionstowindvectorretrieval.
2003. Its objective is to measure the partially polarized emis-
sionfromtheoceansurfaceand,therefore,testandfullyeval-
uate the viability of using polarimetric microwave radiometry II. REGRESSIONTECHNIQUE
to retrieve ocean surface wind vectors. WindSat measures po- Forthisinitialvalidationoftheregressiontechnique,weused
larized radiometric brightness temperatures in 16 “chan- WindSat averagedtothelowestresolution(6.8GHz),where
nels”: vertically and horizontally polarized ( and ) at the forall16channelsareavailable.The foreachchannel
6.8,10.7,18.7,23.8,and37.0GHz,andthirdandfourthStokes are sampled at approximately 12.5 km along scan and along
parameters( and )at10.7,18.7,and37.0GHz. and track and averaged to a common spatial resolution of approx-
arederivedfromthedifferenceof linearandleft/rightcir- imately 40 60 km. The nominal measurement noise for the
cularpolarizations,respectively.TheWindSatreflectorantenna retrievalcells,specifiedbythenoise-equivalentdifferentialtem-
hasan11-feedhorncluster,resultinginfixednominalEarthin- perature(NEDT)formeanoceanscene ,isgiveninTableI.
cidenceangles(EIA)rangingfrom50 to56 (allhornswithina The regression training (i.e., regression coefficient determi-
frequencybandhavethesameEIA)[1].TheEIAsareknownto nation)isperformedempirically,usingcollocationsofWindSat
better than 0.05 [2]. A detailed description of the radiometer with various sources of satellite retrievals and numerical
can be found in [1]. WindSat provides risk reduction for the weather prediction model (NWP) data (Section V). Unlike re-
ConicallyScanningMicrowaveImager/Soundertobeflownon gressionstrainedusing simulatedwithaphysicalmodel[4],
theNationalPolar-orbitingOperationalEnvironmentalSatellite [5],empiricalregressionsautomaticallycompensateforbiaser-
System[3]. rorsinthemeasurements,andpotentialproblemssuchastime-
We have developed an empirical statistical regression tech- dependentcalibrationerrorsorhighnoiseresultsindeemphasis
niqueforretrievalofseasurfacetemperature(SST),windspeed (coefficientsnearzero)fortheaffectedchannel.Therefore,we
useall16channelsinallregressions.Regressioncoefficientsare
determinedbylinearleastsquares.
ManuscriptreceivedDecenber21,2004;revisedAugust5,2005.Thiswork Weusealinearmethodtoretrieveparametersforwhichthe
wassupportedinpartbytheU.S.NavyunderGrantN000WX04730023. relationship with the ranges from nearly linear to severely
C.K.SmithwaswithComputationalPhysics,Inc.,Springfield,VA22151
nonlinear. Three techniques exist to address this nonlinearity
USA. He is nowwith The Aerospace Corporation,Los Angeles, CA90009
USA. problem.Thefirstusesfunctionsofthe thatarelinearwith
M. Bettenhausen and P. W. Gaiser are with the Remote Sensing Divi- respecttoagivenretrievalparameterasargumentsintheregres-
sion, Naval Research Laboratory, Washington DC, 20375 USA (e-mail:
sions [4], [5]. Additionally, terms that are quadratic or higher
[email protected]).
DigitalObjectIdentifier10.1109/LGRS.2005.860661 orderinthe orthe linearizationfunctionsmay beincluded
1545-598X/$20.00©2006IEEE
Report Documentation Page Form Approved
OMB No. 0704-0188
Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and
maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information,
including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington
VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it

does not display a currently valid OMB control number.
1. REPORT DATE 3. DATES COVERED
JAN 2006 2. REPORT TYPE 00-00-2006 to 00-00-2006
4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER
A Statistical Approach to WindSat Ocean Surface Wind Vector Retrieval
5b. GRANT NUMBER
5c. PROGRAM ELEMENT NUMBER
6. AUTHOR(S) 5d. PROJECT NUMBER
5e. TASK NUMBER
5f. WORK UNIT NUMBER
7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION
Naval Research Laboratory,Remote Sensing Division,4555 Overlook REPORT NUMBER
Avenue, SW,Washington,DC,20375
9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR’S ACRONYM(S)
11. SPONSOR/MONITOR’S REPORT
NUMBER(S)
12. DISTRIBUTION/AVAILABILITY STATEMENT
Approved for public release; distribution unlimited
13. SUPPLEMENTARY NOTES
The original document contains color images.
14. ABSTRACT
15. SUBJECT TERMS
16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF 18. NUMBER 19a. NAME OF
ABSTRACT OF PAGES RESPONSIBLE PERSON

a. REPORT b. ABSTRACT c. THIS PAGE 5
unclassified unclassified unclassified
Standard Form 298 (Rev. 8-98)
Prescribed by ANSI Std Z39-18
SMITHetal.:STATISTICALAPPROACHTOWINDSATOCEANSURFACEWINDVECTORRETRIEVAL 165
toallowtheregressiontrainingtoeliminatesomeofthenonlin- The regressions are accomplished using a two-stage tech-
earity directly [5]. The second technique is to retrieve a func- nique. In the first stage, the retrievals are performed for ,
tionofthedesiredparameter,onwhichthe haveanapprox- , and , using regression coefficients trained on the entire
imatelylineardependence[5].Thethirdmethodistotrainand trainingdataset(SectionV).Inthesecondstage,thefirst-stage
applyregressionsonrestricteddomains(“bins”)ofageophys- regressionresultisusedtodetermineawindspeedbin,from
ical parameter on which the have a nonlinear dependence. which regression coefficients are selected to perform the SST
The bins are chosen so that the dependence of the on that and through retrievals.Thebinsare2m/swide,andcover
parameterisapproximatelylinearwithineachbin[4]. thewindspeedranges0–2,2–4,4–6m/s,etc.
The application of these techniques to the retrievals is ex- The second-stage regressions offer significant advantages
plained below. We make a significant extension to the second over analogous first-stage regressions. Within each bin the
techniquetoretrievewinddirection,forwhichthesignalinall Fouriercoefficientsin(2)areapproximatelylinearfunctionsof
the isseverelynonlinear(infactnonunique)anddependson —i.e.,thewinddirectionsignalsareapproximatelylinearin
windspeed.There,weperformregressionsforseveralfunctions through —improvingthe through linearregression
ofwindspeedanddirection,andthenusetheregressionresults andMLEwinddirectionperformance.
in a maximum-likelihood estimator (MLE) for wind direction Second-stageperformanceisimprovedbywideningthebins
retrieval,asdiscussedinSectionIII. during regression training, to account for the “misbinning”
Theregressionforanyretrievalparameter isoftheform resulting from retrieval error in the first-stage regression
[0.8 m/s root mean square (rms) error]. However, the benefit
of accommodating misbinning is counterbalanced by the in-
(1) creasingnonlinearityof the withrespectto through
as the bins are widened. Given the bin width of 2 m/s for the
retrievals, a bin width of 4 m/s for the training is optimal; the
where is the WindSat brightness temperature for channel
binsarethuswidenedby1.0m/soneachsideforsecond-stage
, except for the 23.8-GHz channels, where
training(0–3,1–5,3–7m/s,etc.).
; the latter serves to linearize with respect to
the V signal when water vapor absorption is appreciable [4],
III. MAXIMUM-LIKELIHOODESTIMATOR
[5], which significantly improves the V retrieval but does not
significantly affect any of the other retrievals. The quadratic Following application of the second-stage regressions,
terms greatly reduce the nonlinear interactions (“crosstalk”) through areprovidedtotheMLEforwinddirectionretrieval.
betweenSST,W,V,andL.InclusionoftheEIAforthe37-GHz Thelikelihoodfunctiontobemaximizedisforestimatingtwo
vertical–horizontal(vh)feedhorn(e)reducestheSSTretrieval parameters from four “measurements” ( through
error by 5%, but has little effect on the other retrievals. The ) whose errors are correlated and approximately Gaussian
small variations from the nominal EIAs for the different feed- distributed[9].Therefore,theMLEfindsmultiplewindvector
hornsshowalinearrelationshiptoeachother,sothatinclusion solutions, pairs known as “ambiguities,” as local
ofasingleEIAissufficientforalinearregression.Theregres- minimaof
sionsallowfordirectretrievalofSST,W,V,andL.
The directional component of the microwave sea surface
emissivity canbeexpressedasatwo-termFourierseries
where
in relative wind direction ( , the compass wind direction
minus radiometer look direction projected on the Earth) [6].
TheFouriercoefficientsarenonlinearfunctionsofwindspeed
thatdependonEarthincidenceangle,frequency,andpolariza-
tion [6]. The series is even for and and odd for and
[6],[7]
is the error covariance matrix of the through re-
trievals for the bin determined by the first-stage regression
(Section II) and is obtained by running the regressions on the
(2) trainingdataset(SectionV).The vectorissimplythediffer-
ence between the through regressions and their mathe-
Thus, for retrieval, we perform four regressions, for the maticaldefinitionsgiveninSectionII.
along-look wind speed , cross-look wind The MLE may appear to be a two-dimensional search for
speed , and “second harmonics” of the chi-squareminimainthespaceof .However,itshould
along- and cross-look wind speeds ( and be noted that, because through are linear in , mini-
). The along-look wind speed is identical to mization along the axis yields a single, analytical solution
the“line-of-sight”windspeedretrievedbyWentz[8].Whilethe for at each value of , denoted . Substitution
and regressionsaresufficientforuniquewinddirection of into the definition of the chi-square results in a
retrieval, we find significant improvement for m/s by one-dimensionalminimizationalongthe axis.Thisone-di-
usingallfourregressionsasinputtoanMLEforwinddirection mensionalminimizationissubjecttothefollowingconditions:
retrieval. 1) isnonnegative(physicalsolution);2)thesecond
166 IEEEGEOSCIENCEANDREMOTESENSINGLETTERS,VOL.3,NO.1,JANUARY2006
partialderivativeofthechi-squarewithrespectto ,evaluated SSM/I.GDAS1 1 analysesspatiallyinterpolatedtothelo-
at ,ispositive(trueminimumandnotasaddleorin- cationofWindsatmeasurementsareusedforSST,andforwind
flectionpoint). vectorswhenaQuikSCATobservationisnotavailable.Thecol-
The resulting ambiguities are then ranked locationwithSSM/IisincludedtofacilitatedevelopmentofV
by chi-square, with the “first rank” solution having the lowest andLregressions.Thewindvectortestingdatasetisatwo-way
chi-square.Itshouldbenotedthatthe through regressions collocationbetweenWindSatandQuikSCAT.Forbothtraining
have significantly larger retrieval errors than the first-stage andtesting,thespatialcollocationwindowis25kmforalldata
regression, and therefore the ’s are inferior to the first- sources, and the temporal collocation window is 60 min for
stage regression result. We therefore use the first-stage GDASandQuikSCATand35minforSSM/I.Beforecolloca-
retrievalasthefinalretrievalresult. tion,theeightretrievalcellsalongbothedgesoftheQuikSCAT
swathwereremoved,becausetheycontainlessthantheoptimal
fourbeamcombinations,andhavedegradedwindvectors[11].
IV. MEDIANFILTER
Theregressiontrainingdatasetisqualitycontrolledthrough
Duetoregressionerrors,thefirstrank(mostlikely)solution exclusion of every condition listed in the next paragraph and
is not always the closest ambiguity to the true wind direction. SDRsthatarelikelyinfluencedbythermalgradientsinthewarm
Improvementinambiguityselectionbeyondthefirstrankresult load.Nosuchexclusionsaremadeinthetestingdataset;instead,
ismadeinretrievalpostprocessing,whereweapplyavectorme- qualitycontrolflagsaresetduringretrieval.
dianfilter(MF)[10]tocorrectisolatedambiguityselectioner- RetrievalsareperformedforallSDRsexceptthoseintheaft
rorswhileretainingthelargescalediscontinuities(forexample, scan [1], with surface type other than ocean (no coast or near
fronts)foundinnaturalwindfields. coast), with one or more outside the physical bounds for
The MF is an iterative spatial filter that selects from the set no or light rain, or with any EIA outside the expected range
ofambiguitiesinthecellbeingfilteredtheonemostconsistent (i.e.,morethan0.5 fromnominal).Thefollowingareflagged
with the previously selected ambiguities in a window of sur- as“lowconfidenceretrievals”:1)inlakesorinlandseas;2)re-
rounding cells. It accomplishes this by minimizing the vector trieval cell may contain rain; 3) in areas found to periodically
medianfilterfunction[10],aweightedsumofnormsofvector suffer from radio-frequency interference (RFI) or ice contam-
differences between an ambiguity and the previously selected ination; and 4) are likely to suffer land contamination (within
ambiguitiesforallcellsinthewindow.AftertheMFisapplied 105kmor1.75retrievalcellHPBWfromnearestland).Condi-
to allocean cells inan orbit,the newly selected wind fieldre- tion(2)isdeterminedusinganSSMI-typerainflaggingadapted
placesthepreviouswindfield.Theprocessisiterateduntilthe toWindSatfrequencies[13].Static,geographicalmasksdefine
windfieldconverges. theotherconditions.
Inourapplication,thewindowis7 7cells(inscan-based
coordinates) centered on and including the cell being filtered. VI. RETRIEVALPERFORMANCE
Missingcellsinthewindow(duetoscanedges,land,ice,etc.),
Theretrievalperformanceinthissectionexcludeslowconfi-
are “replaced” with the ambiguities from the center cell. Be-
dence retrievals, which are 20% of the total.1 The Faraday ro-
cause wind direction retrieval performance improves with in-
tationcorrectionnecessarytooptimize retrievalperformance
creasing (Section VI), the term for each cell in the filter
isundervalidation,andhasnotbeenappliedtotheSDRsinthe
function is weighted linearly by the retrieved wind speed for
regressiontestingandtrainingdatasets.
thatcell; forhighconfidencere-
Wind vector retrieval errors are computed relative to collo-
trievals,andhalfthatforlow-confidenceretrievals(SectionV)
catedQuikSCATdata(SectionV).TheQuikSCATtestingdata
andforreplacements.
haserrorsrelativetoinsitugroundtruth,asdoestheQuikSCAT
The MF is initialized with the first rank wind vector from
andGDASdatausedforwindvectorregressiontraining.How-
each ocean retrieval cell. Optionally, it can be initialized with
ever,theerrorscomputedhereshouldbegoodapproximations
nudgedwindvectorfields[11];thenudgedfieldisobtainedby
(and likely upper bounds) to those that would be obtained by
selectingthefirstorsecondrankambiguityforeachcellthatis
trainingandtestingwithactualgroundtruthdata(orbytraining
closesttothewindvectorobtainedfromtheNCEPGlobalData
with simulatedusingaphysicalmodelcalibratedtoground
AssimilationSystem(GDAS)1 1 analysisclosestintime,
truth,andtestingwithgroundtruth).
spatiallyinterpolatedtothecelllocations.
Table II shows the overall regression retrieval errors. Given
theconsiderationsabove,theoverall rmserroriswellwithin
V. ALGORITHMTRAININGANDTESTING 1.0 m/s. Training the regression on a natural, nonuniform
distributionofwindspeeds(ascontainedinthecollocateddata)
Six months of WindSat version 1.6.1 Sensor Data Records
initially resulted in a large bias error at high wind speeds
(SDRs),spanningaperiodwhenthermalgradientsinthewarm
( m/sat23m/s).Wegreatlyreducedthisbias(attheexpense
load[12]areminimal(September2003throughFebruary2004),
havebeencollocatedwithvarioussourcesofNWPandsatellite
retrievalsforuseinregressiontrainingandretrievaltesting(al- 189% of low confidence retrievals were flagged for possible rain, 3% for
ternatingtwodaystraining,onedaytestingthroughout). RFI/ice,10%forlandcontamination,and1%forinlandlakesandseas;mul-
tipleflagscanbesetforeachretrieval.Therainflagwasdevelopedforfiltering
ThetrainingdatasetconsistsofcollocationsbetweenWindsat
allraincasesfromthetrainingdata,andthereforeerrsonthecautioussideand
SDRs,theQuikSCATscienceproduct[11]forwindvectors,and overestimatestheoccurrenceofrain.
SMITHetal.:STATISTICALAPPROACHTOWINDSATOCEANSURFACEWINDVECTORRETRIEVAL 167
TABLE II
OVERALLRETRIEVALERRORSFORREGRESSIONS
Fig.2. WindSatwinddirectionrmserrorsstratifiedbyQuikSCATwindspeed.
ErrorsaredeterminedrelativetothecollocatedQuikSCATscienceproduct.
Fig.1. WindSatwindspeederrorsstratifiedbyQuikSCATwindspeed.
ofintroducingasmalloverallbias)byweightingeachofthecol-
locatedobservationsintheregressiontrainingbythereciprocal
ofthesquarerootofthewindspeeddistribution[14].Thestrat-
ification of the retrieval errors versus the QuikSCAT wind
speedisshowninFig.1.
The overall regression errors for through in Table II
aremuchlargerthanthosefor .Thisoccursbecausethewind
speed signal in is large [5], while the directional signals
Fig. 3. Wind direction skill and mean number of ambiguities stratified by
are relatively small [6], [15]. The through regressions
QuikSCATwindspeed.
must extract parts of the small directional signals, while com-
pensating for atmospheric and SST variations. Because of the ambiguities is low. Due to the requirement that the MLE-re-
sine/cosine dependence of the wind direction signals, the trieved wind speed be nonnegative (Section III), there are no
and regressionsusemainly and ,whilethe and
morethantwoambiguitiesforanyretrieval,2withanaverageof
regressionsusemainly and .Becausethewinddirection
1.56overallretrievals.Thus,themedianfiltercannotimprove
signalsfor and aresuperposedonalargeandhighlyvari- 44%oftheretrievals.Anotherreasonisthat,whenoneconsiders
ablenondirectionalcomponent,theregressionerrorsfor and
only retrievals with two ambiguities, the skill is reduced from
arelargerthanthosefor and .
that shown in the figure; for example the FR skill drops from
Fig.2showsthewinddirectionrmserrorsforthefirstranked
84%to76%at5m/s.
(FR), median filtered (MF), median filtered with nudging Additionally, MF wind fields show good qualitative agree-
(MFNG) ambiguities, and the closest ambiguity to the collo-
ment with QuikSCAT wind fields except inside some regions
catedwinddirection(CL).Thewinddirectionbiaserroratall where m/sandwinddirectionischangingrapidly;there,
issmall(lessthan3 )andisnotshown.
low-skilldegradedretrievalsoftenoccurincontiguouspatches.
Above12m/s,theagreementbetweenthewinddirectionre- By itself, the median filter rarely improves upon the FR am-
trievals and QuikSCAT is excellent for all of the above ambi-
biguityinsuchpatches.Analogoustoscatterometerretrievals,
guities.Ourinitialwinddirectionperformanceiswithin20 of
wehavefoundthattheseparationbetweenthefirstandsecond
QuikSCATforwindspeedsabove9.9,8.8,7.6,and7.2m/sfor rankambiguitiesisabout180 ,whichmayfurtherinhibitme-
the FR, MF, MFNG, and CL ambiguities, respectively. Below dianfilterperformanceinandnearthepatches.Nudging,which
5 m/s, the wind direction signals are very small [6], [15], and can select the second rank ambiguity to initialize the median
thewinddirectionretrievalperformancedegradesaccordingly.
Fig.3showstheskill(percentageofretrievalswherethese- 2Withoutthisrequirement,themaximumnumberofambiguitiesis3.How-
ever,ambiguitieswithnegativeW alwayshavethehighestchi-squareof
lected ambiguity is the CL ambiguity) for the FR, MF, and (cid:0)
anyambiguity(oftenbyanorderofmagnitude),haveavaluenear 1m/s,
MFNG ambiguities, and the total number of ambiguities. De- regardlessofthemagnitudeofthewindspeedscomputedfrom(U ;U )and
spite high FR skill, the median filter by itself makes only a (U ;U )vectors,andappearwhenthesetwowindspeedsaregrosslyincon-
sistent.Thus,weconsiderambiguitieswithnegativeW tobeanunphysical
smallimprovementinthewinddirectionerror(6 to8 below
resultobtainedwhentheMLEisgiveninconsistentvaluesofU throughU .
8 m/s, less than 3 above). One reason is that the number of Assuch,theyshouldbediscarded.
168 IEEEGEOSCIENCEANDREMOTESENSINGLETTERS,VOL.3,NO.1,JANUARY2006
filter,allowssignificantimprovementintheagreementbetween REFERENCES
MFNG and QuikSCAT wind fields over the patches, and thus
[1] P.W.Gaiser,K.St.Germain,E.M.Twarog,G.A.Poe,W.Purdy,D.
in wind direction performance at low and intermediate wind Richardson,W.Grossman,W.L.Jones,D.Spencer,G.Golba,M.Mook,
speeds. J.Cleveland,L.Choy,R.M.Bevilacqua,andP.Chang,“TheWindSat
spacebornepolarimetricmicrowaveradiometer:Sensordescriptionand
Becausenudgingchoosesbetweentwoambiguitiesseparated
earlyorbitperformance,”IEEETrans.Geosci.RemoteSens.,vol.42,no.
byapproximately180 ,smalltomoderateperturbationstothe 11,pp.2347–2361,Nov.2004.
windfieldusedfornudgingwillhavelittleeffectonthenudged [2] W.E.Purdy,P.W.Gaiser,G.A.Poe,E.A.Uliana,T.Meissner,and
F.Wentz,“GeolocationandpointingaccuracyanalysisfortheWindSat
windfieldusedtoinitializethemedianfilter[11].Therefore,the
sensor,”IEEETrans.Geosci.RemoteSens.,vol.44,no.3,Mar.2006,to
incorporationofQuikSCATdataintoGDASwindfieldsshould bepublished.
notsignificantlyaffectthenudgedwindfield,orthecomparison [3] D.B.Kunkee,N.S.Chauhan,andJ.J.Jewel,“Phaseonedevelopmentof
theNPOESSConical-ScanningMicrowaveImager/Sounder(CMIS),”
oftheMFNGambiguitieswithQuikSCAT.
inProc.IGARSS,vol.2,Toronto,ON,Canada,Jun.24–28,2002,pp.
1005–1007.
[4] T.T.WilheitandA.T.C.Chang,“Analgorithmforretrievalofocean
VII. CONCLUSION
surfaceandatmosphericparametersfromtheobservationsoftheScan-
ningMultichannelMicrowaveRadiometer,”RadioSci.,vol.15,no.3,
We have developed a regression retrieval technique for
pp.525–544,May–Jun.1980.
WindSat retrieval of ocean surface wind speed and a com- [5] F.J.WentzandT.Meissner,“AMSROceanAlgorithm,Version2,”Re-
bination regression/MLE technique for ocean surface wind moteSensingSystems,SantaRosa,CA,AlgorithmTheoreticalBasis
Document,Tech.Rep.121599A,Nov.2000.
direction retrieval. The regressions were derived empirically,
[6] S. H.Yueh, W. J. Wilson, S. J. Dinardo, and F. K. Li,“Polarimetric
using collocated NWP data and satellite-based retrievals. To- microwavesignaturesofoceanwinddirections,”IEEETrans.Geosci.
gether, they provide an initial assessment of WindSat retrieval RemoteSens.,vol.37,no.2,pp.949–959,Mar.1999.
[7] S.H.Yueh,R.Kwok,andS.V.Nghiem,“Polarimetricscatteringand
capabilities.ComparisonsoftheinitialWindSatretrievalswith
emissionpropertiesoftargetswithreflectionsymmetry,”RadioSci.,vol.
QuikSCAT wind vectors have demonstrated the capability for 29,no.6,pp.1409–1420,Nov./Dec.1994.
passivemicrowavewinddirectionretrieval,andshowexcellent [8] F.J.Wentz,“Awell-calibratedoceanalgorithmfortheSpecialSensor
Microwave/Imager,”J.Geophys.Res.,vol.102,no.C4,pp.8703–8718,
agreementwithQuikSCATwindspeeds.
Apr.1997.
Weexpecttheretrievalstoimproveduetoongoingworkon [9] C.D.Rodgers,InverseMethodsforAtmosphericSounding:Theoryand
the SDR algorithm, quality control flagging, and the retrieval Practice. Singapore:WorldScientific,2000,pp.20–25,32,66,84.
[10] S.J.Shaffer,R.S.Dunbar,S.V.Hsiao,andD.G.Long,“Amedian-filter-
algorithms. Ongoing work on the SDR algorithm includes ad-
basedambiguityremovalalgorithmforNSCAT,”IEEETrans.Geosci.
dition of corrections for Faraday rotation and thermal gradi- RemoteSens.,vol.29,no.1,pp.167–174,Jan.1991.
entsinthewarmcalibrationload.Wearealsoworkingonim- [11] B.W.Stiles,B.D.Pollard,andR.S.Dunbar,“Directionintervalre-
trievalwiththresholdnudging:Amethodforimprovingtheaccuracyof
proved rain flagging, and replacement of the sea ice and RFI
QuikSCATwinds,”IEEETrans.Geosci.RemoteSens.,vol.40,no.1,
geographical masks with dynamic ice and RFI detection. Re- pp.79–89,Jan.2002.
trievalalgorithmworkincludesusingnonuniformbinwidthsin [12] E.Twarog,P.W.Gaiser,B.Purdy,L.Jones,K.St.Germain,andG.Poe,
“WindSatpost-launchcalibration,”inProc.IGARSS,Anchorage,AK,
the second-stage regressions, tuning the median filter, and in-
2004,pp.375–378.
corporating buoy observations into the regression training and [13] M.H.Bettenhausen,C.K.Smith,R.M.Bevilacqua,N.-Y.Wang,P.W.
testing. Gaiser,andS.Cox,“AnonlinearoptimizationalgorithmforWindSat
windvectorretrievals,”IEEETrans.Geosci.RemoteSens.,vol.44,2006,
tobepublished.
ACKNOWLEDGMENT [14] L.N.ConnorandP.S.Chang,“Oceansurfacewindretrievalsusingthe
TRMMMicrowaveImager,”IEEETrans.Geosci.RemoteSens.,vol.38,
The authors would like to thank L. Connor for his work in no.4,pp.2009–2016,Jul.2000.
assembling the collocated datasets, S. Cox for performing the [15] T.MeissnerandF.Wentz,“Anupdatedanalysisoftheoceansurface
winddirectionsignalinpassivemicrowavebrightnesstemperatures,”
erroranalysis,andP.Craneforassistingininitialdevelopment
IEEETrans.Geosci.RemoteSens.,vol.40,no.6,pp.1230–1240,Jun.
oftheregressionsoftware. 2002.