The ADM-Aeolus wind retrieval algorithms

15 Pages·2008·1.52 MB·English

Tellus(2008),60A,191–205 (cid:2)C 2007TheAuthors
Journalcompilation(cid:2)C 2007BlackwellMunksgaard
PrintedinSingapore.Allrightsreserved
TELLUS
The ADM-Aeolus wind retrieval algorithms
∗
By DAVID G. H. TAN1 , ERIK ANDERSSON1, JOS DE KLOE2, GERT-JAN MARSEILLE2,
AD STOFFELEN2, PAUL POLI3, MARIE-LAURE DENNEULIN3, ALAIN DABAS3, DORIT
HUBER4, OLIVER REITEBUCH4, PIERRE FLAMANT5, OLIVIER LE RILLE6 and HERBERT
NETT6, 1ECMWF,ShinfieldPark,ReadingRG29AX,UK;2KNMI,Postbus201,3730AE,deBilt,theNetherlands;
3Me´te´o-France,42avenueCoriolis,31057Toulouse,France;4DLR,Oberpfaffenhofen,D-82234Wessling,Germany;
5LMD/IPSL,91128Palaiseau,France;6ESA/ESTEC,Postbus299,NL-2200-AGNoordwijk,theNetherlands
(Manuscriptreceived15January2007;infinalform20August2007)
ABSTRACT
TheADM-AeolusisprimarilyaresearchanddemonstrationmissionflyingthefirstDopplerwindlidarinspace.Flex-
ibledataprocessingtoolsarebeingdevelopedforuseintheoperationalgroundsegmentandbythemeteorological
community.Wepresentthealgorithmsdevelopedtoretrieveaccurateandrepresentativewindprofiles,suitableforas-
similationinnumericalweatherprediction.Thealgorithmsprovideaflexibleframeworkforclassificationandweighting
ofmeasurement-scale(1–10km)dataintoaggregated,observation-scale(50km)windprofilesforassimilation.The
algorithmsaccountfortemperatureandpressureeffectsinthemolecularbackscattersignal,andsothemainremaining
scientificchallengeistoproducerepresentativewindsininhomogeneousatmosphericconditions,suchasstrongwind
shear,brokenclouds,andaerosollayers.TheAeolusinstrumentprovidesseparatemeasurementsinRayleighandMie
channels,representingmolecular(clearair)andparticulate(aerosolandclouds)backscatter,respectively.Thecombin-
ingofinformationfromthetwochannelsofferspossibilitiestodetectandflagdifficult,inhomogeneousconditions.The
functionalityofabaselineversionofthedevelopedsoftwarehasbeendemonstratedbasedonsimulationofidealized
cases.
measurementsandobservations2in24layerswithconfigurable
1. Introduction
verticaldistributionthatcanbemodifiedinflight.Thecurrent
TheAtmosphericDynamicsMission,ADM-Aeolus,isthefourth baseline configuration will provide 1000 m vertical resolution
ofESA’sEarthExplorerMissions1(ESA,1999;Stoffelenetal., throughmostoftheatmosphere(from2to16km),500mbelow
2005a).ADM-Aeolusisscheduledforlaunchinmid-2009and 2km,and2000mbetween16and26km(Fig.1).
hasaprojectedlifetimeofthreeyears.Itsobjectiveistodemon- TheschematicinFig.1showstheDWLinstrumentviewing
stratethecapabilitytomeasurewindprofilesfromspaceusinga fromalow-altitude(∼400km)polarorbitinthedirectionper-
DopplerWindLidar(DWL).Theneedforsuchdata,withhigh pendiculartothesatellitetrack.Measurementsaremadeintwo
accuracyandgoodverticalresolution,hasbeenidentifiedasapri- receiver‘channels’:RayleighformolecularreturnsandMiefor
orityfortheglobalobservingsystem(WMO,2004).Themission particulates.Thereisinformationonthehorizontalline-of-sight
objectivesandobservationrequirementshavebeendesignedto (HLOS)windcomponentonly(line-of-sightwindvelocitydi-
meetscientificgoalsinusercommunitiesinclimateresearch,at- vided by the cosine of the local elevation angle ∼53◦), which
mosphericmodellingandnumericalweatherprediction(NWP). is close to east–west except at high latitudes. The unobserved
The polar orbit facilitates the global data coverage that is re- wind component and the mass field will have to be statisti-
quired,providingdataalsoovertheoceanswhicharecurrently callyinferredwithinthedataassimilationprocess(Riishøjgaard
poorly observed. The DWL will provide layer-averaged wind etal.,2004;Zˇagar,2004;Stoffelenetal.,2005b;Zˇagaretal.,
2005;Tanetal.,2007).Theinstrumentwillprovide50kmalong-
track average winds, separated by 150 km data gaps (Fig. 1);
thisistoensureminimalerrorcorrelationbetweenconsecutive
∗
Correspondingauthor.
e-mail:[email protected] 2Theterm‘measurement’isusedforinstrumentdatacharacterizedby
DOI:10.1111/j.1600-0870.2007.00285.x horizontalscalesofbetween1and10km,whereas‘observation’isused
1ESAEarthExplorerswebsite:www.esa.int/esaLP/LPearthexp.html. foraggregateddataathorizontalscalesof50km.
Tellus60A(2008),2 191
192 D. G. H. TAN ET AL.
km
2126.5
Molecular Layers
2024.5
1922.5
1820.5
Aerosol Layers 1718.5
km
16.521 1616.5
15.520 1515.5
14.519 1414.5
13.518 1313.5
12.517 1212.5
11109...555111654 1110911910.5..55 Direction to sun
8.513 88.5
7.512 77.5
65..551110 6565..55 Velocity direction
4.59 44.5
--0132210.......10505505587654321 WGS 832143210....5555 of sight 496 km35° Altitude 400 km
e
n
Li
z0 Nadir
285 km Fig.1. Line-of-sightviewinggeometryand
50 km (7 sec) proposedverticaldistributionoftherange
200 km (28 sec) Horizontal line of sight bs(ERhiSnoaAswy.(lielnaiggyhethr)sec)hafaeonrrontsheoleslA(sMeDpiMaer)a-aAteneldyo.lmuCsoolsuearcttueellsalyirteo,f
Level 2B retrieved
horizontal line of sight
wind profiles,
at 50 km scale
Auxiliary
Level 1B Parameter settings
meteorological data Rayleigh Brillouin
ADM measurements controlling the
(T and p at ADM correction coefficients
At 3.8 km scale processor options
locations)
Predicted orbit
positions to be used in
case of missing orbits
Fig.2. SchematicshowingmaininputstotheADM-AeoluswindretrievalalgorithmandtheoutputL2Bdata.Unshadedboxesindicatethat
geolocationinformationisusedtodeterminethelocationsofauxiliarymeteorologicaldata.
observations (Stoffelen et al., 2005a) and maximize the infor- molecules(i.e.thedensityofair)andattenuationbyoverlying
mationcontentwhileconservingtheenergyconsumptionofthe aerosolandcloud.TheexpectedyieldandaccuracyofAeolus
instrument. The accuracy of the ADM-Aeolus wind measure- winds has been studied through detailed simulation (Tan and
mentsandobservationswilldependprimarilyontheintensity Andersson,2005),basedonmodelclouds(fromtheEuropean
of the backscattered laser light, which in the Mie channel de- CentreforMedium-RangeWeatherForecasts,ECMWF)andcli-
pendsonthepresenceandopticalthicknessofclouds,andthe matologicalaerosol(Vaughanetal.,1995,1998)distributions.
concentrationofaerosol(MarseilleandStoffelen,2003),andin The literature cited above has noted that ADM-Aeolus of-
theRayleighchannelitdependsmainlyontheconcentrationof fers substantial complementarity to existing wind observing
Tellus60A(2008),2
THE ADM-AEOLUS WIND RETRIEVAL ALGORITHMS 193
Diameter
7.5 mm
Front optics
Interference filter (1 nm)
Field stop
Diameter 20 mm
Aperture stop
/2
/4 Rayleigh
spectrometer (RSP)
diameter 36 mm
/ 4
thermal control
Mie cocoon
spectrometer
(MSP)
DFU1 Detection Front
End Unit (Rayleigh)
diameter 4 mm
DFU2 Detection Front
End Unit (Mie)
Fig.3. OpticalreceiverarchitecturefortheAeolusDWLinstrument.CourtesyofAstriumSatellites.
systems—toradiosondes,windprofilersandaircraftdatabypro- rabletotheradiosonde/windprofilernetwork,withthebenefits
viding global coverage especially over oceans and away from beingmostapparentoveroceansandintheTropics.Anunderly-
theprincipalflightroutes,andtoatmosphericmotionvectorsby ingassumptionofsuchstudiesisthatthedataprocessingchain,
providing profiles with good vertical resolution. Complemen- fromrawinstrumentdatauptoLevel-2Bandincludingthegen-
taritytomass/temperatureobservingsystems,thatis,radiance eration of calibration/characterization data, is able to produce
andtemperaturedata,hasalsobeennoted—thisisregardedas sufficientlyaccurateproducts(errorsinHLOSwindestimates
particularly valuable for determining atmospheric motion on shouldbebelow2ms−1throughoutmostoftheatmosphere).
sub-synoptic scales and in the Tropics, that is, for regimes in InthispaperwedescribetheADM-AeolusLevel-2B(L2B)
which temperature data and conventional mass/wind balance windretrievalalgorithmswhichformpartoftheADM-Aeolus
relationships are inadequate (both empirically and from theo- dataprocessingchain.Thepurposeofthesealgorithmsistoob-
retical/dynamical arguments). Weissman and Cardinali (2007) tainrepresentativeandaccuratewindssuitableforuseinNWP.
showedthatDWLobservationstakenintheNorthAtlanticfrom Level-1B(L1B)windretrievalsarenotsuitableforuseinNWP
anairborneplatformhadasignificantpositiveimpactonanalyses for a number of reasons, the principal one being that L1B al-
andforecastsoftheECMWFforecastsystem.Increasingly,sim- gorithmsdonotaccountexplicitlyfortemperatureandpressure
ulatedAeolusdataarebeingevaluatedagainstrealobservations effectsontheresponseofthemolecular(Rayleigh)channelof
in NWP data assimilation/forecast experiments. For example, theinstrument(seecompanionpaperDabasetal.,2008).The
Tanetal.(2007)developedatechniquebasedonthespreadof L2BalgorithmsuseNWPinformationtotaketheseeffectsinto
anensembleofdataassimilations,tocomparetheexpectedim- account.ThedesignoftheL2Balgorithmstakesaccountofthe
pactofAeolusdatatothatoftheradiosondeandwindprofiler technicalcapabilitiesandconstraintsoftheinstrument,forexam-
network.TheyfoundthatAeoluscanbeexpectedtoreduceanal- plewithrespecttoverticalandhorizontalsampling,instrument
ysisandshort-rangeforecastuncertaintybyanamountcompa- pointingstabilityandzerowindcalibration.Qualitycontroland
Tellus60A(2008),2
194 D. G. H. TAN ET AL.
product confidence indicators are important items that will be onaerosolandcloudlayeropticalproperties)aredescribedby
providedwiththewindretrievals.Inbrokencloudscenes,itis Flamantetal.(2008).
envisagedthatseparatewindretrievalswillbederivedforclouds The operational production of L2B data will be done at
andclearair.Thiswillbedonethroughselectiveaveragingof ECMWFslightlybehindrealtime,justbeforetheassimilation
measurement-scaledatainthelayersofclearairaboveclouds, (andproductionofL2C)iscarriedout.TheL2Bprocessinguses
from cloud-top layers, from layers in and below thin clouds, aprioriinformationonthestateoftheatmosphereatthetime
andfromlayerswithsufficientaerosolinthelowerpartsofthe andplaceoftheAeolusL1Bmeasurements.Thisinformation
atmosphere. isbestprovidedbythebackgroundfieldsoftheNWPsystem,
TheADM-Aeolusisprimarilyaresearchanddemonstration thatis,thefieldspredictedbytheforecastmodelrunfromthe
missionthatwillprovidemanyopportunitiesforassessingthe previousanalysis.Meteorologicalbackgrounddata,interpolated
benefitsofspace-basedwindprofileinformation,andfordefin- intheverticalplanealongtheflighttrackwillalsobecreatedand
ingthestepstowardsfutureoperationalDWLmissions.Given deliveredtoESAtofacilitatere-processingoftheAeolusL1B
theexperimentalnatureofthemission,ithasbeenrecognized dataatalatertime,andforoff-linecalibrationtasks.Figure2is
thatdataprocessingneedstohavesufficientflexibilitytoexplore aschematicdiagramshowingthevariousdatasetsinvolvedin
the full potential of the mission data. The L2B wind retrieval creatingtheL2Bdata.Itisenvisagedthatfortheirownpurposes,
algorithmsdiscussedhereinarelikelytoevolveduringthemis- manymeteorologicalcentresotherthanECMWFwillproduce
sion. The evolution is expected to be relatively minor, but of L2B data with local background inputs, and according to the
course any changes will be thoroughly documented. The L2B timelinessconstraintsoftheirownoperationalNWPsystems.
softwarewillbefreelyavailabletothemeteorologicalcommu-
nity.Thesoftwarehasbeendesignedtobeportable,andspecifi-
2.1. TheADM-Aeolusinstrument
callytoruninthreedifferentcontexts:(1)real-timeprocessingat
NWPcentreswithaninteresttoassimilateADM-Aeoluswinds ThepayloadoftheADM-AeolusmissionisasingleDWLin-
withintheirownforecastingsystems;(2)operationalprocessing strument.Theinstrumentisahigh-spectralresolutionlidaroper-
attheECMWFtoproducewindretrievalsfordeliverytoESA atingintheultraviolet,atwavelengthλ =355nm.TheDoppler
0
shortly after real time and (3) re-processing at ESA for situa- frequencyshifts(cid:3)ν ofthereturned(elasticbackscatter)atmo-
tionsinwhichdelaysindatadeliverypreventprocessingwithin spheric signals provide profile information on wind velocity
theECMWFoperationalschedule,andtoaccommodatefuture alongtheinstrument’sline-of-sightv ,
LOS
algorithmimprovementsandupgrades.
v
ThescopeandpurposeoftheL2Bwindretrievalprocessoris (cid:3)ν =−2 LOS (1)
λ
describedinSection2.Detaileddescriptionsofthealgorithms 0
are given in Section 3. Examples illustrating the behaviour of whilethesignalamplitudesprovideinformationonparticlelay-
theretrieval,classificationanderrorestimationsareprovidedin ersandtheiropticalproperties.Thesignalamplitudesalsopro-
Section4,followedbyconcludingremarksinSection5. videproductconfidencedata,includingerrorquantifiers,forthe
windandparticleinformation.
Althoughmoredetailsonthemeasurementprinciplesunder-
2. ThescopeandpurposeoftheAeoluswind
pinningtheinstrumentaregiveninotherpapersfromthisvol-
retrievalalgorithms
ume (Dabas et al., 2008; Flamant et al., 2008) and elsewhere
Thealgorithmsoutlinedinthispaperareinvolvedincalculating (Reitebuchetal.,2006),wesummarizeherethepointsmostrel-
theL2BHLOSwindobservationsatthe50kmscalebasedon evantforunderstandingtheLevel-2Bwindretrievalalgorithms.
ADM-Aeolusmeasurementsandinstrumentperformancedata. Thesolidcurveinfig.1fromDabasetal.(2008)showsanom-
Theywerederivedprimarilytoformpartofapieceofsoftware inalfrequencyspectrummeasuredbytheinstrumentwhilethe
that creates the ADM-Aeolus Level-2B (L2B) data products. dashedcurveshowshowthespectrumisshiftedinthepresence
Basedonthecalibratedmeasurements(L1B)asinputs,theyap- ofa50ms−1 LOSvelocity.Thespectralreturnfromparticles
plythemodifications,correctionsandadditionsrequiredtoob- (aerosol,cloud)contributesthesharpnarrowpeaksandthespec-
tainaccurateandrepresentativeHLOSwindssuitableforassimi- tralreturnfrommoleculescontributesthebroadportions(nearly
lationbyNWPsystems,aswellastheappropriatequalitycontrol Gaussianbutmodifiedbytemperatureandpressureeffects).The
flags and uncertainty estimates. Key features of Aeolus prod- instrumentincludesbothaMiereceiverandaRayleighreceiver
uctsaresummarizedinTable1.Theso-calledLevel-2C(L2C) designed to detect, respectively, the particulate and molecular
productisasupersetoftheL2Bproductandwillbedescribed returnsignals(Fig.3).TheMiereceiverisbasedonthefringe
elsewhere.Briefly,itcontainsadditionaloutputfromECMWF imagingtechniquewithaFizeauinterferometerusedinamode
assimilation of L2B data, that is, ECMWF analysed winds at whereitformsaninterferencefringewhosespatialpositionis
the Aeolus data locations. Thus, L2B products are intermedi- wavelengthdependent,thatis,aDopplershifttranslatesintoa
atebetweenL1BandL2Cdata.Level-2Aproducts(information lateraldisplacementofthefringe.TheRayleighreceiverisbased
Tellus60A(2008),2
THE ADM-AEOLUS WIND RETRIEVAL ALGORITHMS 195
Table1. ThemainADM-Aeolusdataproducts
Typicalsize
(Megabytesper
Productlevel Description orbit) Comments
Level1B Engineering- 21–70 Near-real-timeproduct.Spectrometerdataat
corrected measurementscale,HLOSwindprofilesusing
HLOSwinds algorithmsthatdonotaccountexplicitlyfor
sceneclassificationnorforRayleigh-Brillouin
(pressure/temperature)effects.
Level2A Aerosolandcloud 7–10 Off-lineproduct.SeeFlamantetal.(2008)
layeroptical
properties
Level2B Meteorologically- 13–18 Shortlyafternear-real-timeforoperational
representative products(generatedatECMWF),potentially
HLOSwinds near-real-timeforothermeteorological
centres(dependingonschedule).HLOSwind
profilesusingalgorithmsthat(a)group
measurementsaccordingtoa
scene-classificationprocedureand(b)account
explicitlyforRayleigh-Brillouin
effects—makinguseofNWPestimatesof
atmospherictemperatureandpressure,
typicallyfromashort-rangeforecast.Subset
ofLevel2Cproducts.
Level2C Aeolus-assisted 19–24 SupersetofLevel2Bproducts.AddsECMWF
windvectors analysedwinds(2horizontalcomponents)at
theADM-Aeoluslocations,and
supplementaryproductconfidencedata
derivedduringassimilationofLevel2Bdata
atECMWF.Theanalysedwindstakeinto
accountotheratmosphericobservationsand
theECMWFforecastmodelthroughthedata
assimilationscheme.
onthedouble-edgetechniquewithasequentialFabry-Perot,it- sistingofabout200independentobservations.BoththeL1Band
selfincludingtwoband-passfilters‘A’and‘B’whichproduce L2BobservationscalewindretrievalarecomputedfromNMie
two signal outputs that are then used in ecartometry mode to andRayleighmeasurements,eachmeasurementitselfbeingthe
estimate the Doppler shifts. The two filters are centred on the resultoftheon-boardanalogueaccumulationofPlaserreturns
edgesofthebackscatteredmolecularspectrumandplacedsym- withanACCDdetector(seeFig.4).Inthestandardoperating
metrically with respect to the laser central wavelength. Using mode of the Lidar, N = 14 (up to 20 if laser warm-up pulses
thereflectiononinterferometersandpolarizationopticstoper- are considered) and P = 50, defining what is called the ba-
formthespectralseparation,theatmosphericreturnsignalfirst sic repeat cycle (BRC). Given the satellite ground-velocity of
enterstheMiereceiverandsubsequentlytheRayleighreceiver. ∼7.6 km s−1, and a pulse repetition frequency (PRF) of
ThedetectorusedforbothchannelsisanAccumulationCharge 100 s−1, one measurement integrates the atmospheric return
CoupledDevice(ACCD)witha16by16pixelsusefulimage over a horizontal distance of (P = 50) × (7.6 km s−1)/
zoneandoptimizedforultravioletsensitivity. (PRF = 100 s−1) = 3.8 km. The N contiguous measurements
thatmakeuponeobservationarerepresentativeofthewindfield
over a horizontal distance (N = 14) × 3.8 km ≈ 50 km (or
2.2. Measurements,observationsandwindretrievals
≈70 km if warm-up pulses are kept and N = 20). Note that
L1BdataaretheinstrumentalinputtotheL2Bprocessor.The thestartingpointsoftwoconsecutiveobservationsareseparated
L1B data set contains Aeolus measurements and observations by 200 km, that is to say there is a 150 km data gap between
andassociatedauxiliaryparametersofoneorbit,typicallycon- consecutive50kmobservations.
Tellus60A(2008),2
196 D. G. H. TAN ET AL.
1 BRC = 1 observation = N measurements
Measurements 1 2 3 N-1 N
….. ….. ….. ….. …..
P laser pulses
Fig.4. DiagramshowingthegeometryofADM-Aeolusmeasurementsandobservations.Duringonebasicrepeatcycle(BRC)ofthelaser,N
measurementsareacquiredeachresultingfromtheon-boardanalogueaccumulationofPatmosphericreturnsgeneratedbyPlaserpulses.OneBRC
correspondsto50kmontheground(Fig.1).
N L1 measurements
top
s
n
bi
ht
g
ei L2bP
h
e h
d g
Altitu aylei
R
or
e
Mi
4
2
base
Fig.5. SchematicillustrationoftheselectiveaveragingofNmeasurementsat24levels(orheightbins,left)inasingleAeolusobservation(over50
km)intoseveralpartialorcompletewindprofileretrievals(right).Thegreyshadingrepresentsabrokencloudlayerandthecolouredsquares
representclassificationofindividualmeasurementsincategories:greenrepresentsmeasurementsabovethebrokencloudlayer,yellowarethecloud
topreturns,redarebetweenbrokenclouds,andbluearecloudfree.
The L2B processor has access to the L1B data at mea- 2.3. L2Bprocessing
surement scale (3.8 km). This gives essential information on
Inthissection,wedescribetheeffectsandinfluencesthatneed
the heterogeneities of the atmosphere within one observation
tobeaccountedforwithinthecalculationofAeolusL2Bwind
(50 km) and can help detect those situations which may lead
retrievals.
to large measurement or representativeness errors in wind re-
2.3.1. Spatialcoordinates. TheverticalcoordinateofAeolus
trievals.Inordertocreaterepresentativeaverageswithinanob-
L1B data is the height above the reference WGS84 ellipsoid
servation, the measurements may be grouped into several cat-
whereas NWP models typically work with heights above the
egories each containing a profile of Mie and Rayleigh winds
meansea-level.Thedifferencebetweenthetwoaltitudesmay
measuredinsimilarhomogeneousconditions.
beseveraltensofmeters,whichislessthanthethicknessofrange
Anidealizedmeteorologicalsituationthatwouldresultinsev-
bins(250m–2km)butstillcannotbeneglectedincontextswith
eralseparatewindretrievalswithina50kmAeolusL2Bobser-
strongverticalgradientsinthewind.Aconversionhasthusbeen
vationisillustratedinFig.5.TheNMie(orRayleigh)measure-
implemented which takes as reference the EGM96 geoid (see
mentsformingtheobservationsareshownontheleft.Singleal-
http://cddisa.gsfc.nasa.gov/926/egm96/).
titude,brokencloudscanbeseeninthelefthalf,blockingsome
2.3.2. Cross-talk between the Mie and Rayleigh channels.
(in this case every second) of the measurements while others
L2BprocessingofMiechanneldataneedstoaccountforcontri-
penetratetothesurface.Themeasurementsingreenabovethe
butionsfromthebroadreturnedRayleighspectrum;suchcontri-
cloudlayerareaveragedlevel-by-leveltoformthefirstretrieval
butionsaregenerallyregardedasonecomponentofbackground
containingonlythethreetop-mostlevels.Thecloudreturns(yel-
light. L2B processing of Rayleigh channel data needs to ac-
low)aregroupedintoasecondprofile,inthiscaselimitedtoa
countfortemperatureandpressuredependenceofthemolecular
singlelevel.Themeasurementsfromclearairbetweenorbelow
backscatter(Rayleigh-Brillouin).Italsoneedstoaccountforthe
clouds (red) are averaged to form the third profile with valid
possibilitythatreflectionsfromtheMiechannel’sFizeauinter-
windsfromthesurfacetothealtitudeofthecloudlayer.Finally,
ferometerentertheRayleighchannel.
measurementsfromthesecond,homogeneoushalfofthescene
2.3.3. Temperatureandpressureeffects. L1BRayleighwinds
(blue)producethefourthwindprofileretrieval.
areobtainedbyconvertingtheresponseofthedualFabry-Perot
Tellus60A(2008),2
THE ADM-AEOLUS WIND RETRIEVAL ALGORITHMS 197
(definedbythedifferenceofphotonscountedattheoutputof tivecontributionofparticlebackscatterdoesnotneedtobevery
Fabry-PerotAandBdividedbytheirsum)intoaDopplershift accurate.
throughtheuseofaresponsecalibrationcurve.Thetrueresponse 2.3.4. Effect of atmospheric heterogeneities. An essential
curvedependsontheatmosphericconditionsinthesensingvol- taskoftheL2Bprocessoristheidentificationoftheatmospheric
umeviatheshapeofthetemperature-andpressure-dependent situationsthatarelikelytoproducelargeerrorsintheretrieved
spectrum of the Rayleigh-Brillouin spectrum of the scattered windvelocities.Cloudandaerosolstructuresareoftenspatially
light.Inthepresenceofaerosolandclouds,Miesignalcontami- intermittentleadingtopotentiallylargesignaldifferencesfrom
natestheRayleighsignal,therebymodifyingtheresponsecurve onemeasurementtothenextatthesameheight.Measurements
ofthedoubleFabry-Perot.TheRayleighwindsmusttherefore withrelativelylowsignal-to-noiseratiocanpotentiallydeterio-
becorrectedforpressure,temperatureandresidualMielightef- ratethewindestimateduetotheirhighnoisecontributions.Here
fects.DetailsofthecorrectionschemearepresentedbyDabas signal-to-noiseratioisdefinedastheusefulsignaldividedbythe
etal.(2008).Asitrequirespriorknowledgeontheactualtemper- detectionnoiseassociatedwiththeusefulsignalandbackground
atureandpressureinsidethesensingvolume,itwillnecessarily light.DatapointswithstrongMiereturnsmaybeflaggedbecause
beappliedatL2BlevelwhichhasaccesstoNWPdata.Foreach ofthepotentialfortheMiereturnstocontaminatethedetected
Rayleighwind,theoutputwillbe signalintheRayleighchannels.LargevariabilityofstrongMie
returnslikelysignalsthepresenceofturbulenceandverticalmo-
(i) A Rayleigh HLOS wind corrected from pressure and
tion.Insuchcases,theoptimalweightingofthestrongandweak
temperatureeffects,usingpriorestimatesP andT .
ref ref MieandRayleighmeasurementshastobedonewithgreatcareto
(ii) Thederivativea ofthecorrectedwindwithrespectto
R ensurethatspatiallyrepresentativewindestimatesareobtained.
theRayleighresponse(neededforerrorquantifier,seebelow).
Stratification of aerosol or cloud structures within a range
(iii) Thederivativea ofthecorrectedwindwithrespectto
T bin(layer)maycausestrongverticalgradientsintheextinction
thetemperature.
profileresultinginverticallynon-uniformcontributionsoflaser
(vi) Thederivativea ofthecorrectedwindwithrespectto
p light returning from within that range bin. In such conditions
thepressure.
theretrievedwindwouldnotrepresentthetruemeanwindover
thelayer.Withoutknowledgeaboutthedetailedstratificationthe
Theselasttwocoefficients(sensitivities)permitoptionalfurther
windretrievalcanonlybeassignedtothemid-pointofthelayer,
refinementofthecorrectionwhenincrementallyimprovedtem-
potentiallyincurringasignificantheightassignmenterror.How-
peratureandpressureestimatesbecomeavailableduringNWP
ever,itmaybepossibletoflagtheconditionsinwhichthistype
assimilationofatmosphericobservations:
ofheightassignmenterrorislikelytooccur,bycarefulscrutiny
v (T,P)=v (T ,P ) ofdifferencesinreturnsbetweensubsequentrangegatesinthe
HLOS HLOS ref ref
+a (T −T )+a (P−P ). (2) MieandRayleighchannels.Rayleighsignalheightassignment
T ref p ref
problems could also be tackled by redistributing the available
Itisworthnotingthattheuseofmeteorologicalparameters(tem- (24)Mierangebinstoover-samplepartsofthetroposphere.
peratureandpressureestimates)intheL2Bwindretrievalim-
pliessomecorrelationoferrorsbetweenthewindestimatesand
3. HLOSwindretrieval—thedetails
theNWPmodelsupplyingthemeteorologicalparameters.The
correlationisdirectlyproportionaltothesensitivitycoefficients InthissectionwegivedetailsoftheL2Bwindretrievalsfromthe
reportedintheL2Bproduct,andisinfactrathersmall—ascan MieandRayleighchannels,togetherwiththeirassociatederror
beinferredfromfigs.5and7ofthecompanionpaperbyDabas quantifiers.L2Bproductscontainotherancillaryparametersof
etal.(2008). amoretechnicalnature,whichwillbedescribedelsewhere.For
ThecorrectionoftheimpactofMieresidueintheRayleigh the sake of brevity we do not include in this paper a number
signalscanbecarriedoutatthesametime.Thisrequiresinforma- ofpreliminarystepsrelatedtothequalitycontrolandscreening
tionontheamountofMielightatthereceiver.Thisinformation ofLevel-1Binputdata.Broadlyspeaking,theseinvolvecheck-
cannot be obtained from the NWP model because the aerosol ingthatthevariousparametersofinterestarewithinreasonable
backscatter is not a model parameter and it cannot easily and bounds.Precisedetails,includingthethresholdvaluesapplied,
reliablyberelatedtoexistingmodelparameters:itmustbeesti- can only be given after the mission’s in-orbit commissioning
matedfromthemeasuredsignalsthemselves,whichinprinciple phase.
ispossiblebycomparingthestrengthofthesignalsregistered Intheintroductionwereferredtotheneedforflexibilityinthe
ontheMieandRayleighchannel(theformeronebeingmostly dataprocessing.ThisflexibilityismanifestedintheHLOSwind
sensitive to particle backscatter while the latter one is mostly retrievalalgorithmsthroughtheirformulationintermsofweights
representativeofthemolecularreturn).Notethat,duetothelow wi,k giventorangegate(orlayer)iofmeasurementk.Thetwo
sensitivityoftheRayleighresponsetoMiecontamination(see indicesarethusindicativeofthetwodirections:iisinthevertical
companionpaperDabasetal.2008),theestimationoftherela- andkinthehorizontal.Forclarityofexposition,wepresentthe
Tellus60A(2008),2
198 D. G. H. TAN ET AL.
windretrievalalgorithmsintermsofweightsthatareassumed Non-linearitiesarecharacterizedregularlybyapropercalibra-
given.Wefollowthiswithabaselinespecificationforhowthese tion procedure. It remains to specify how δm and δm are
ATMi REFi
weights are assigned for the examples presented in Section 4, computed.ThesearetheresultofapplyingtheMiecorealgo-
andindicatewhatweconsidertobethemostpromisingoptions rithm (Reitebuch et al., 2006) to the spectrometer readouts r
foralternativespecificationstobeexploredduringthemission (vectorofthen=16numbersofphotonscountedbythen=16
lifetime. accumulationCCDpixels):
δm =Mie Core[r ] (9)
ATMi ATMi
3.1. L2BMiechannelHLOSwindestimate
δm =Mie Core[r ]. (10)
The Mie-channel retrieved HLOS wind (vHLOS) is computed REFi REFi
fromtheatmosphericreturndetectedbytheMiechannel(vATM) TheinputstotheMieCorealgorithmaretheweightedspectrom-
and three correction terms. Two of these correction terms ac- etermeasurements(subscriptk)fortheatmosphericreturnand
countforthelaserinternalreferencepath(vREF)andthesatellite theinternalreference:
velocity relative to the ground (vSAT). The third correction is (cid:4)N (cid:4)N
knownasthegroundwind(vG)correctionterm,andcorrectsfor rATMi = wi,krATMi,k; rREFi = wi,krREFi,k. (11)
instrumentaloffsetsthatariseevenforanatmosphericvolume k=1 k=1
inwhichthewindvelocityiszero,asexpectedfortherangebins ThespacecraftLOSvelocityv isgivenbysimilarlyweighting
SAT
that intercept the Earth’s surface. Consequently, the retrieved themeasurement-scalevelocities:
MieHLOSwindisobtainedby (cid:4)N
vHLOSi =cvoLOsSϕi (3) vSATi = k=1wi,kvSATi,k (12)
v =v −[v +v +v ], (4)
LOSi ATMi REFi SATi G
3.2. L2BRayleighchannelHLOSwindretrieval
where ϕ is the local elevation angle and i is the range-bin (or
layer)index. TheL2BretrievedRayleighchannelHLOSwindaccountsfor
TheatmosphericandinternalreferenceLOSvelocities,v the atmospheric return detected by the Rayleigh channel and
ATM
andv ,arecomputedfromtheDopplershiftδbytwosimilar threecorrectionterms.AsinthecaseofMiewindretrieval,these
REF
expressions: correctiontermsaccountforthelaserinternalreferencepath,the
satellitevelocityrelativetothegroundandtheoffsetsthatarise
1
v =− λ δ /s (5) evenforanatmosphericvolumeinwhichthewindvelocityis
ATMi 2 0 ATMi ATM
zero.Consequently,theL2BRayleighHLOSretrievalisdefined:
(cid:2) (cid:3)
1 1
v =− λ δ /s . (6) v =v − v +v +v . (13)
REFi 2 0 REFi REF HLOSi ILIADi cosϕ REFi SATi G
Thefactor−λ0/2,withthewavelengthλ0 =355nm,converts Here,vILIADrepresentstheHLOSwindretrievaltakingintoac-
DopplershiftsintoLOSwindvelocities.TheDopplershiftin countthetemperatureandpressuredependenceofthemolecular
frequencyunitsisgivenbytheratiooftheDopplershiftinCCD backscatter(Rayleigh-Brillouin).Thev velocityisobtained
ILIAD
pixelunitsδ,andaninstrumentresponseparameters(tothefirst byapplyingtheso-calledILIADschemetotheRayleighchannel
orderequaltothefrequencyspanγ =1500MHzoftheCCD, measurements.ILIADtakesthegeneralform:
theso-calledusefulspectralrange,dividedbytheusefulnumber
n=16ofCCDpixels).Fortheatmosphericpathandtheinternal vILIADi =ILIAD[RATMi,ρi,Ti,pi]. (14)
referencepaththesearedenotedbysubscriptsATMandREF, Details of the ILIAD scheme are given in Dabas et al. (2008)
respectively. In an ideal instrument, the Doppler shift in pixel sowhatisneededhereistospecifytheinputparameters.These
unitswouldbethepixelpositionofthepeakoftheinterference parametersdenoteweightedvalueswithinthescatteringvolume,
fringeoutputbytheFizeauinterferometerandimagedontothe for, respectively, the so-called instrument Rayleigh response
accumulationCCD,whichwedenotebyδm.However,toaccount R , the scattering ratio ρ = 1 + β /β (where β and β
ATMi a m a m
fornon-idealnon-linearresponseoftheactualinstrument,the aretheparticleandmolecularbackscattercoefficients,respec-
Dopplershiftinpixelunitsδiscomputedfromδmwithafurther tively),thetemperatureTandthepressurep.Theweightedval-
non-linearitycorrectionE: uesareobtainedfromaweightedaverageofthecorresponding
(cid:2) (cid:3)
quantitiesatthemeasurement-scale:
δ =δm −E δm . (7)
ATMi ATMi ATM ATMi (cid:4)N (cid:4)N (cid:4)N
δ =δm −E (cid:2)δm (cid:3). (8) ρi = wi,kρi,k,Ti = wi,kTi,k,pi = wi,kpi,k. (15)
REFi REFi REF REFi k=1 k=1 k=1
Tellus60A(2008),2
THE ADM-AEOLUS WIND RETRIEVAL ALGORITHMS 199
Similarly, the weighted Rayleigh response needed as input to TheseexpressionsarederivedasdescribedintheAppendix.It
ILIADisgivenintermsoftheweightedsummationofuseful isworthnotingthatthesummationindexin(18)runsfrom3to
signals in channels A and B (numbers of photons counted at 18—indices1and2correspondtotheCCD‘pre-pixels’which
theoutputofFabry-PerotAandBcorrectedfrombackground arenotusedforwindprocessing.
noise),NAandNB:
NA−NB 3.4. ErrorquantifierfortheL2BRayleighchannel
RATMi = NiA+NiB, (16) HLOSwindestimate
i i
wheretheweightedsummationusefulsignalinchannelsAand TheinversionstepoftheILIADschemecomputesthehorizontal
Bare: lineofsightwindvHLOSo usingthesensitivity∂vHLOSo/∂p,fora
(cid:4)N (cid:4)N zeroscatteringratio,ρ=0.Foragivenscatteringratio,ρ(cid:7)=0,
NiA = wi,kNiA,k; NiB = wi,kNiB,k. (17) alinearcorrectionisapplied
Compukt=at1ionofameteorologkic=a1llyweightedinternalreference vHLOS=vHLOSo +ρ∂vH∂LρOSo. (22)
LOSvelocityv andthesatellite’sLOSvelocityv areper-
formedinananRaElFogousmanner,usingtheweightswSiA,kT. RThera(dsieaelweqi.nd16v)H,LtOhSeotiesmcopmerpaututeredTfroamndththeeRparyelsesiugrherpe.spFornosme
ATM
thisfollowsthattheerroronthecalculatedHLOSwindis
(cid:3)v = ∂vHLOSo(cid:3)R + ∂vHLOSo(cid:3)T
3.3. ErrorquantifierfortheL2BMiechannelHLOS HLOS ∂RATM ATM ∂T
windestimate +∂vHLOSo(cid:3)p+ρ∂vHLOSo, (23)
∂p ∂ρ
ThebaselinefortheL2BMieerrorquantifieristheerrorstandard
deviationσ givenbytheequation: where∂vHLOSo/∂T and∂vHLOSo/∂parethelocalsensitivitiesof
MIE (cid:5) (cid:6) (cid:7)(cid:8) the vHLOSo to T and p, respectively. For uncorrelated errors in
σMIEi = 2cλo0sϕ (cid:4)18 αi2,j −1(cid:8)(cid:9)(cid:4)18 σi2,jαi2,j, (18) sthceatrteesriunlgtinrgatHioL,OteSmeprerroart,uσreH,LOpSre=ssu√re(cid:9)(cid:3)anvdHLROSa(cid:3)ylveHigLhOSr(cid:10)ewspiothns(cid:9)e(cid:10),
j=3 j=3 theoperatorfortheexpectedcovariance,isestimatedas
(cid:19)
(cid:20) (cid:21) (cid:20) (cid:21) (cid:20) (cid:21) (cid:20) (cid:21)
σHLOS= ∂∂vRHLOSoσRATM 2+ ∂v∂HLTOSoσT 2+ ∂v∂HLpOSoσp 2+ ∂vH∂LρOSoσρ 2. (24)
ATM
where The sensitivities ∂vHLOSo/∂·are delivered as output from the
(cid:4)N ILIADscheme.Thedominanttermstemsfrom∂vHLOSo/∂RATM,
σ2 = w2 r (19) whichisabout296ms−1.Thetermswiththestandarddeviation
i,j k=1 i,k ATMi,j,k oferrorfortemperatureandpressure,respectivelyσTandσpare
is an estimate for the variance of the weighted sum of photo- smallbutthisneedstobefurthertested.Equation(24)assumes
counts r after removal of a detection chain (or analogue) thaterrorsintheelevationangleϕarenegligible,contributinga
ATM
offset,and winderroroforder0.01ms−1.
⎡ ⎤
Itremainstoprovideanexpressionfortheerrorstandardde-
αi,j =τj H ⎢⎣1+ 4(cid:13)f1j+−ˆfiMIE(cid:14) − 1+ 4(cid:13)f1j−−ˆfiMIE(cid:14)⎥⎦. (20) fvoialltoiownsothfatthethreeeturrronrsiingnRaAlTRMAiTsM.Fromasensitivityanalysisit
fw2 fw2 (cid:3)R = ∂RATM (cid:3)NA+ ∂RATM (cid:3)NB
HereH,f and(cid:18)fMIE(infrequencyunits)aretheoutputsofthe ATM ∂NA ∂NB
w i
Mie core algorithm (H is peak height and fw is the full width = 2NB (cid:3)NA− 2NA (cid:3)NB. (25)
athalfmaximumofthefittedLorentzianspectrum,and(cid:18)fMIEis (NA+NB)2 (NA+NB)2
i
theMiefrequencyestimate),τjisacorrectionfactoraccounting Forindependenterrorsin(cid:22)NA andNB theestimatederrorinthe
fortheobscurationoftheprimarymirrorofthetelescopebythe responseR , σ = (cid:9)(cid:3)R2 (cid:10)isobtainedby
tripodthatbearsthesecondarymirror(characterizedatground ATM RATM(cid:23) ATM
2
before launch), and f+j , f−j are the upper and lower frequen- σRATM = (NA+NB)2 NB2σA2+NA2σB2. (26)
ciesofCCDpixelindexj.Withtheusefulspectralrangeγ =
RecallfromSection3thatNAandNBareobtainedfromweighted
1500MHz:
γ γ γ γ sums of NA and NB, respectively, where each of the measure-
fj−=(j−3)16 − 2 and fj+=(j−2)16 − 2. (21) mentsinrakngegatkeihasafractionalweightwi,k.σA andσB,
Tellus60A(2008),2
200 D. G. H. TAN ET AL.
atobservation-scale,arethusaswellobtainedfromaweighted
BRC1 BRC2 BRC3 BRC4
sum,respectively
(cid:4)N (cid:4)N d2 d3 d4 d5
σA2 = wi2,kσA2,k σB2 = wi2,kσB2,k, (27) Clou Clou Clou Clou
k=1 k=1
whereσA,k andσB,k arethestandarddeviationsofNkA andNkB Clear Cloud1
(equaltothesquarerootofNAandNBundertheassumptionthat
k k
thephotoncountuncertaintyisgovernedbyPoissonstatistics).
Fig.6. Schematicrepresentationofthetestscenariowiththreecloud
layers.
3.5. Selectiveaveraging
Through aggregation of L1B data at measurement scale, L2B dertomakeL2Bobservations.Thebaselinescenarioismadeof
algorithmscanproducemultiplewindretrievalsatobservation- fourconsecutiveobservations(orBRCs)wheretheHLOSwind
scale,asillustratedinFig.5.Theprocedureconsistsoftwomain isconstantforallaltitudes(50ms−1).ThefourBRCscontaina
steps: classification and weighting of each measurement-scale seriesofthreecloudlayerslocatedatthealtitudes4,9and15km
rangegate. asillustratedinFig.6.ThefirstBRCisclear(seeFig.7forthe
correspondingscatteringratioprofile);thesecondispartlycov-
(i) Classification.Thescatteringratioestimateavailablein eredbyCloud1featuringasinglecloudlayerat4kmaltitude;
L1Bproductsisanindicatorofthepresenceofparticles(cloud the third is entirely covered by Cloud1, and finally the fourth
oraerosol)withintherange-gate.Thebaselineclassificationex- BRC contains five different cloud scenarios formed through a
ploits this by classifying all range-gates with scattering ratio combination of partly overlapping clouds at three levels. The
aboveathresholdvalueasbeing‘Cloudy’,andthosebelowthe scenariocalledCloud2containstwocloudlayersat4and9km
thresholdas‘Clear’ altitudes, and so on. Briefly, the Cloud1, Cloud3 and Cloud5
(ii) Weighting. Let N Cloudy denote the number of mea- BRCscontainone-layerclouds,whileCloud2andCloud4con-
surementsforwhichrange-gateiisclassifiedas‘Cloudy’,and taintwo-layerclouds.
let N Clear denote the number of measurements for which ThescenariodescribedaboveisrunthroughtheAeolusEnd-
range-gate i is classified as ‘Clear’. Then two sets of weights to-End simulator (E2S v2.01) and L1B Processor (v1.05), de-
are defined: W cloudy(i,k) = 1/N Cloudy when range-gate i veloped in the frame of the satellite prime contract, in order
of measurement k is classified as ‘Cloudy’, and 0 otherwise. to generate Level-0 and Level-1 instrument data. The Level-1
W clear(i,k)=1/N Clearwhenrange-gateiofmeasurementk products are then used as input by the L2B processor (v1.2).
isclassifiedas‘Clear’,and0otherwise.Accordingtothissim- The classification criterion uses the scattering ratio calculated
ple weighting scheme, each measurement range-gate receives bytheL1Bprocessorforeachmeasurementrange-gatewitha
non-zeroweightinjustonesetofweights. thresholdof1.5toseparateclearfromcloudy.
UponrunningtheL2Bprocessor,theclassificationprocedure
Flexibility for future modifications includes classification
assignseachmeasurementrange-gatewiththreepossiblevalues:
basedonL2Bretrievalofopticalparameters.Theweightsmaybe
adjustedbasedonthesignal-to-noiseratioofeachmeasurement-
scalerange-gate. 30
Clear
Cloud 1
4. Applicationtosimulateddata 25 Cloud 2
Cloud 3
Cloud 4
The sensitivities of the baseline L2B algorithms to basic as- m)20 Cloud 5
sumptions,errorsanduncertaintiesinitsmaininputshavebeen e (k15
testedthroughcarefulsimulationsandapplicationtoidealized ud
cases.Twotestsaredescribedheretodemonstrateretrievalof Altit10
HLOSwinds,sceneclassificationwithselectiveaveraging,and
estimationofwindretrievaluncertainty. 5
0
4.1. Sceneclassificationandselectiveaveraging 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Thepurposeofthistestistoassessthesceneclassificationabil- Fig.7. ScatteringratiooftheobservationsClear,andCloud1to
itytoidentifyclearfromcloudyareasinanacademicscenario Cloud5.Thecloudprofileshaveeachbeenshiftedbyoneunitinthe
andtogroupthemeasurementrange-gatesappropriatelyinor- x-direction,forconvenience.
Tellus60A(2008),2