Assimilation of AIRS/IASI data at ECMWF · Data assimilation system (4D-Var) The observations are...

transcript

AssimilationofAIRS/IASIdataatECMWF P.Bauer ⒸECMWF

Assimilation of AIRS/IASI data atECMWF

PeterBauer,EuropeanCentreforMedium-RangeWeatherForecasts

TonyMcNally,AndrewCollard,MarcoMatricardi,WeiHan,CarlaCardinali,NielsBormann

•Initialperformance/impactassessment•Upgrades:Additionofwatervapourchannels,cloud-affectedradiances,ozone•Comprehensiveobservingsystemexperiments•Futureupgrades•Summary

Data assimilation system (4D-Var)

The observations are used to correct errors in the shortforecast from the previous analysis time.

Every 12 hours we assimilate 4 – 8,000,000 observations tocorrect the 100,000,000 variables that define the model’svirtual atmosphere.

This is done by a careful 4-dimensional interpolation inspace and time of the available observations; this operationtakes as much computer power as the 10-day forecast.

~3,000,000 from AIRS& IASI!

Radiances (→ brightness temperature = level 1):• AMSU-A on NOAA-15/18/19, AQUA, Metop• AMSU-B/MHS on NOAA-17/18/19, Metop• SSM/I on F-15, AMSR-E on Aqua• HIRS on NOAA-17/19, Metop• AIRS on AQUA, IASI on Metop• MVIRI on Meteosat-7, SEVIRI on Meteosat-9, GOES-11/12, MTSAT-1R imagers

Bending angles (→ bending angle = level 1):• COSMIC (6 satellites), GRAS on Metop

Ozone (→ total column ozone = level 2):• Total column ozone from SBUV on NOAA-17/18, OMI on Aura

Atmospheric Motion Vectors (→ wind speed = level 2):• Meteosat-7/9, GOES-11/12, MTSAT-1R, MODIS on Terra/Aqua

Sea surface parameters (→ wind speed and wave height = level 2):• Near-surface wind speed from Seawinds on QuikSCAT, ERS-2 scatterometer,

ASCAT on Metop• Significant wave height from RA-2/ASAR on Envisat, Jason altimeter

Data sources: Satellites

Initial performance assessment

Upgrades: Addition of water vapourchannels, cloud-affected radiances

Comprehensive observing systemexperiments

Future upgrades

Summary

• AIRSCO2andH2OchannelsassimilatedsinceOctober2003(324channels,1/9FOV).• IASICO2/H2OchannelsassimilatedsinceJune2007/March2009(8461channels,1/4FOV).• Assimilatedinclear-skyareasandaboveclouds;sinceSeptember2009infullyovercastsituations,AIRS(notIASI)overlandsurfaces/sea-ice.• Continuousrevisionofchannelusage,qualitycontrol:Ozonechannels,PCRT.

Current use of AIRS/IASI data

λ[μm]

ΔTB[K]

IASI(afterApril2007calibrationchange)

FG-departurestandarddeviation

MeanFG-departureafterbiascorrection

MeanFG-departurebeforebiascorrection

λ[μm]

Noise: AIRS vs. IASI data

(A.Collard)

First-guessdeparturestandarddeviationsin15μmCO2-band

ObservedCalculated

IASI: Model minus observations

(A.Collard)

First-guessdeparturestandarddeviationsinH2O-band

ObservedCalculated

IASI: Model minus observations

(A.Collard)

Future upgrades

Summary

Greychannelsarethe120H2OchannelsdistributedviatheGTS

10IASIwatervapourchannels

IASI H2O channel impact

(A.Collard)

Best value at ~1.5K Normalisedto unity here

10IASIwatervapourchannels:Fittoothermoisturesounderradiances

IASI H2O channel impact

(A.Collard)

AIRSchannel226at13.5micron(peakabout600hPa)

AIRSchannel787at11.0micron(surfacesensingwindowchannel)

TemperatureJacobian(K)

Pressure(hPa)

unaffectedchannelsassimilated

contaminatedchannelsrejected

Anon-linearpatternrecognitionalgorithmisappliedtodeparturesoftheobservedradiancespectrafromacomputedclear-skybackgroundspectra.

Thisidentifiesthecharacteristicsignalofcloudinthedataandallowscontaminatedchannelstoberejected.

obs-calc(K)

Verticallyrankedchannelindex

IASI/AIRS cloud detection

• byaddingcloudtoppressureandeffectivecloudfractiontocontrolvector(viasinkvariable),forretrievedeffectivecloudcover=1;• nocloudyRTcalculationsrequired,conservativelinearizationpoint.

SinglecycleHIRS,AIRS,IASIovercast/clear

Assimilation of cloud-affected channels

(T.McNally)

TemperatureforecasterrorRMSEdifference(EXP-CTRL,77cases,ownanalyses)200hPa

500hPa 700hPa

Positive: deteriorationNegative: improvement

0.2+ K shading

(T.McNally)

Future upgrades

Summary

AIRS/IASI impact

CTRL plus AIRS

(T.McNally)

CTRL plus IASI

(T.McNally)

AIRS/IASI impact

CTRL plus both

(T.McNally)

AIRS/IASI impact

0 2 4 6 8 10 12 14 16 18 20

SYNOP-windAIREP-windDRIBU-windTEMP-windPILOT-windGOES-AMV

MTSAT-AMVMET-AMV

MODIS-AMVSCAT-wind

SYNOP-massAIREP-massDRIBU-massTEMP-mass

HIRSAMSU-A

AIRSIASI

GPS-ROSSMI

AMSR-EMHS

AMSU-BMET 7-RadMET 9-Rad

MTSAT-RadGOES-Rad

0 5 10 15 20 25 30

SYNOP-windAIREP-windDRIBU-windTEMP-windPILOT-windGOES-AMV

MTSAT-AMVMET-AMV

MODIS-AMVSCAT-wind

SYNOP-massAIREP-massDRIBU-massTEMP-mass

HIRSAMSU-A

AIRSIASI

GPS-ROSSMI

AMSR-EMHS

AMSU-BMET 7-RadMET 9-Rad

MTSAT-RadGOES-Rad

FEC per OBS %

Relative FC error reduction per system

Relative FC error reduction per observation

(C.Cardinali)

Advanced diagnostics

The forecast sensitivity(Cardinali, 2009, QJRMS,135, 239-250) denotes thesensitivity of a forecast errormetric (dry energy norm at 24or 48-hour range) to theobservations. The forecastsensitivity is determined bythe sensitivity of the forecasterror to the initial state, theinnovation vector, and theKalman gain.

0 1 2 3 4 5 6 7 8 9

Met-AMV

AMSU-A

GPS-RO

AMSU-B

Met-Rad

MTSAT-

GOES-Rad

black cntrl3 AMSU-A, 2 MHS vs 1 AMSU-A, 0 MHS

(C.Cardinali)

Advanced diagnostics – MW sounder denial

Forecast error reduction [%]

Future upgrades

Summary

Active IASI ozone channel assimilationJacobians of 321 O3channels in 9.6µmband (black: 16selected channels)

Ozone analysis vs.sonde observations(01-02/2009, N.H.)

Observed vs.simulated biasacross O3spectrum (biascorrected), N.H.

(W.Han)

IASI O3-channels

• Baseline System: T511 (40 km) full operational data (no O3 observations)

• UV System: As Baseline plus UV data from SBUV and OMI

• IASI System: As Baseline plus 16 IASI ozone channels (LW cloud detection andchannel 1585 anchored to zero bias correction, other channels VarBC)

Zonal mean cross section of fullozone field (shaded) and meananalysis difference with andwithout IASI ozone channels(units are mass mixing ratio)

(W.Han)

Verification against MLS(2 weeks, 20090615-20090630)

BASELINE = No O3Observations

BASELINE

+ SBUV + OMI

BASELINE

+ IASI 16 O3channels

BIAS: <AN-MLS> Std. dev.: <AN-MLS>

(W.Han)

Impact on T-channels with O3-sensitivityExperiment with anchoring channel 1585 and 15 bias-corrected channels

# 1585 50 hpa (O3)

# 290 340 hPa (T)

IASI data compression

Objective:• DevelopmentofPCradiativetransfermodeltobeabletoassimilatePC-scorecompresseddatafromadvancedinfraredsounders.

• EvaluatemodelwithfocusonshortwaveIASIchannelsandthepotentialofefficientnoisereduction.

IASIchannel6982at4.2μmoriginal reconstructedusing200PCs

(A.Collard)

Fit of RTTOV and PC_RTTOV(400 PCs, 600 predictors) toline-by-line radiances for a5165 profile independent set

(M.Matricardi)

Accuracy of PC radiative transfer

… and the latest …

IASI: Observation errors (σO)

Temperature soundingLW

Window WV

IASI: Spatial error correlations

Temperature soundingLW

Window WV

IASI: Inter-channel error correlations (Desroziers)

4D-Var experiments

• Use Desroziers-estimated observation errors in 4DVAR, with correlations,and scaling factor (July/August 2009).

• Standard deviations of Obs-FG, normalised to 1 for no-IASI experiment:

Chessboard pattern apparent inbackground departurecovariances for almost allassimilated IASI channels.

Pixel numbering:At ECMWF: Only pixel 1 of 4IASI pixels within AMSU-A FOVis currently considered.

ECMWF O-B covariance [K2]for channel 360 (734.750 cm-1)

3Flightdirection

IASI Pixel 1 spatial covariance

(N.Bormann)

Future upgrades

Summary

Advancedsounders(AIRS/IASI)representthemostvaluablesingleinstrumenttypeforNWP

Ideally,AIRS/IASIobservationsarecomplementedbypassivemicrowave(clouds/precipitation),conventionalandradiooccultationobservations(anchoringofbiascorrection)

AtECMWF,IASIobservationusagehasbeenconstantlyimproved/extendedsinceinitialimplementationon12June2007:Clouddetection,qualitycontrol,watervapourchannels,cloud-affectedradiances

Nextdevelopments:• Usageoverland,improvedusageoversea-ice• Moreaggressiveusageofcloud-affectedchannels• Activeusageofozonechannels• TestofPrincipalComponentmodelwithrealobservationsforIASIband3(andfullspectrum)• InclusionofvariableCO2(andothertracegases)

IASI spatial covariance - summary

• Background departure covariances for IASI suggest a smallerror that is correlated between different scan-positions andscan-lines, with alternating positive and negative correlations.

• Similar patterns observed at ECMWF and Met Office.• Effect largest for pixels 1 and 2, little effect for pixels 3 and 4.• The size of the error is small and of no concern to the

assimilation of the data.

• Error appears to be correlated to the direction of themovement of the corner-cube mirror.

• Possibly a signature of micro-vibrations affecting spectralcharacteristics (Denis Blumstein)?

Model grids for T799 and T1279

25 km843,490 points

16 km2,140,704 points

OPS(35r2)

980 hPa

(35r3) Use ofimproved QC(Huber norm)

961 hPa

(36r1) High-res systemT1279+T159/T255/T255

945 hPa

Hurricane Bill,

20 Aug. 2009

Observed MSLpressure~944 hPa

T1279 Tropical cyclone analyses improvedImproved Huber norm QC also beneficial

Impact of resolution upgrade

10 meter wind gustoccurrence probability:

> 24 m/s

> 35 m/s

T399L62 T639L62 (T319L62 10days+)

(R.Buizza)

IASIForecastScores

IASIbetterIASI

worseSH

500hPageopotentialanomalycorrelation(56cases,spring2007,normalizedRMSEdifference,ownanalysis)

Meanerrordifference uncertainty

IASI NWP impact prior to implementation (12/06/07)

(A.Collard)

ECMWF model forecast performance

ECMWF model suites

Analysis Observationwindow Observationfile Extractionstart00DA 21-03 21-03 04:0000DCDA 21-09 21-03 13:45

03-09 14:0012DA 09-15 09-15 16:0012DCDA 09-21 09-15 01:45

15-21 02:0006SCDA 03-09 03-09 10:0018SCDA 15-21 15-21 22:00

ECMWF model suites

Delayedcut-offsuiteprovidesshort-rangeforecastfor…

Earlydeliverysuitethatinitializes…

Medium-rangeforecast

Dataextractionstarttimesforeachsuite:

Data sources: ConventionalSYNOP/SHIP/METAR:

• Meteorological/aeronautical land surface weather stations (2m-temperature,dew-point temperature, 10m-wind)

• Ships→ temperature, dew-point temperature, wind (land: 2m, ships: 25m)

BUOYS:• Moored buoys (TAO, PIRATA)• Drifters→ temperature, pressure, wind

TEMP/TEMPSHIP/DROPSONDES:• Radiosondes• ASAPs (commercial ships replacing stationary weather ships)• Dropsondes released from aircrafts (NOAA, Met Office, tropical cyclones,

experimental field campaigns, e.g., FASTEX, NORPEX)→ temperature, humidity, pressure, wind profiles

PROFILERS:• UHF/VHF Doppler radars (Europe, US, Japan)→ wind profiles

Aircraft:• AIREPS (manual reports from pilots)• AMDARs, ACARs, etc. (automated readings)→ temperature, pressure, wind profiles

Experiment verification

Forecasts:•verificationagainstexperiment’sownanalyses,•verificationagainstoperationalanalyses,•verificationagainstobservations,incl.informationonstatisticalsignificance.

→Accuracy(anomalycorrelation,root-mean-squareerror)ofselectedmeterologicalparameter(T,q,z,R)forecastsatsignificantmodelheights(1000,750,500,200hPa):Betterobservingsystemshouldimproveanalysisandmedium-rangeforecast,i.e.beclosertomeansofverification.

NormalizedRMSEdifference:(RMSEexp–RMSEctrl)/RMSEctrl

Meanerrordifference uncertainty

Experiment verification

Analyses:→Fit(biasandstandarddeviation)ofobservations(in-situandremotelysensed)tomodelfirstguessandanalysis:Betterobservingsystemshouldimproveanalysisandshort-rangeforecast,i.e.drawclosertoentireobserveddataset.

Single-levelobservation

Multiplelevel/channelobservation

ECMWF model forecast performance - NH

RTTOV PC-RTTOV

Simulatederrorcorrelationspectrum:

Principal component radiative transferObjective:FastradiativetransfercalculationsandexploitationofthefullinformationcontentcontainedintheIASIspectrum

Method:• DevelopmentofPCcalculationinsideRTTOVfastmodelwithminimalcodestructuremodification(includingtangent-linearandadjointmodel)• Testaccuracywithrespecttoline-by-lineandconventionalRTTOVradiancecalculations(alsoagainstradianceobservations)• Plans:

• ApplyPC-RTTOVtoIASIshortwaveband(denoising)• Furtherextendtofullspectrum(potentialissueswith:Jacobians,clouddetection,landsurfaces,etc.)

(M.Matricardi)

FirstcycleovercastHIRS,AIRS,IASI•800-600hPa,•600-300hPa,o300-100hPa

FirstcycletemperatureincrementdifferenceEXP-CTRL

250hPa

700hPa

positivenegative

0.2 K intervals

Firstcycletemperatureincrement250hPa(0.2,0.5,1Kintervals)

Experiment Experiment-Control

Control(dotsareovercastdata)

Supporting States and Co-operation

Belgium Ireland PortugalDenmark Italy SwitzerlandGermany Luxembourg FinlandSpain The Netherlands SwedenFrance Norway TurkeyGreece Austria United Kingdom

Co-operation agreements or working arrangements with:Czech Republic Montenegro ACMADCroatia Morocco ESAEstonia Romania EUMETSATHungary Serbia WMOIceland Slovakia JRC Latvia Slovenia CTBTOLithuania CLRTAP

COUNCIL18 Member States

Organisation of ECMWF

DIRECTORD. Marbouty(France) (230)

MeteorologicalDivision

E. Andersson(Sweden) (42)

ComputerDivisionI. Weger

(Austria) (65)

OperationsW. Zwieflhofer

(Austria) (111)

ResearchE. Källén

(Sweden) (90)

AdministrationU. Dahremöller

(Germany) (25)

DataDivision

J.-N. Thépaut(France) (37)

ModelDivisionM. Miller(UK) (24)

Probabilistic Forecastingand Diagnostics Division

T. Palmer(UK) (19)

FinanceCommittee7 Members

Technical AdvisoryCommittee18 Members

Scientific AdvisoryCommittee12 Members

Policy AdvisoryCommittee

7-18 Members

Advisory Committeeof Co-operating States

12 Members

Advisory Committeeon Data Policy

8-31 Members

ECMWF Budget 2009

Germany 20.20%

Denmark1.87%Belgium2.71%

United Kingdom16.43%

Turkey 2.38%

Sweden 2.66%

Finland 1.42%

Switzerland 2.89%Portugal1.29%

Austria 2.16%

Norway 2.13%

Netherlands 4.61%

Italy12.66%

Ireland1.23%

Greece1.74%

France 15.46%

Spain 7.95%Main Revenue 2009

Member States’contributions £35,593,300

Co-operating States’contributions £847,400

Other Revenue £1,169,500

Total £37,610,200

GNI Scale 2009–2011

Luxembourg0.23%

Main Expenditure 2009Staff £14,450,100Leaving Allowances& Pensions £2,965,200ComputerExpenditure £15,690,600Buildings £3,634,300Supplies £870,000

Total £37,610,200

ECMWF model forecast performance - Europe

Assimilation of AIRS/IASI data at ECMWF · Data assimilation system (4D-Var) The observations are...

Documents