Development of AMSU-A Development of AMSU-A Fundamental CDR’sFundamental CDR’s

Huan MengHuan Meng11, Wenze Yang, Wenze Yang22, Ralph , Ralph FerraroFerraro11

11NOAA/NESDIS/STAR/CoRP/Satellite Climate Studies BranchNOAA/NESDIS/STAR/CoRP/Satellite Climate Studies Branch22NOAA Corporate Institute for Climate and SatellitesNOAA Corporate Institute for Climate and Satellites

Huan.Meng@noaa.gov

22

Background: Background: Part of a project supported by the NOAA Climate Data Part of a project supported by the NOAA Climate Data

Record (CDR) programRecord (CDR) program

Goals:Goals:Develop Develop Advanced Microwave Sounding Unit-AAdvanced Microwave Sounding Unit-A and and

–B (AMSU-A/-B) and Microwave Humidity Sounder –B (AMSU-A/-B) and Microwave Humidity Sounder (MHS) FCDR’s for “window” and water vapor channels(MHS) FCDR’s for “window” and water vapor channels

AMSU-A: 23.8, 31.4, 50.3, 89.0 GHzAMSU-A: 23.8, 31.4, 50.3, 89.0 GHzAMSU-B/MHS: 89, 150/157; 183AMSU-B/MHS: 89, 150/157; 183++1, 1831, 183++3, 1833, 183++7/190.3 7/190.3

GHzGHzDevelop TCDR’s for hydrological products (rain, snow, Develop TCDR’s for hydrological products (rain, snow,

etc.)etc.)

Source DataSource Data NOAA-15,16,17,18,19 & MetOp-A L1B dataNOAA-15,16,17,18,19 & MetOp-A L1B data

OverviewOverview

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AMSU-A SensorsAMSU-A Sensors Polar orbiting; cross track scan with 30 FOVs; 48 Polar orbiting; cross track scan with 30 FOVs; 48

km at nadir; “mixed” polarizationskm at nadir; “mixed” polarizations

POES Satellites (carry AMSU-A, -B/MHS):POES Satellites (carry AMSU-A, -B/MHS):

=> NOAA-17 Channels 3 & 15 only have 1 year record=> NOAA-15 with large geolocation error since March 2010

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AMSU-A SDR BiasesAMSU-A SDR Biases Across scan asymmetryAcross scan asymmetry

Changes over orbit (ASC/DSC)Changes over orbit (ASC/DSC)Changes over life of sensorChanges over life of sensor

Warm target contaminationWarm target contamination ((ZouZou et al., et al., to be submitted)to be submitted)Orbital drift + Sun heating + Instrument nonlinear calibration Orbital drift + Sun heating + Instrument nonlinear calibration

errorerror Reflector emission Reflector emission Orbital decayOrbital decay Diurnal driftDiurnal drift Antenna pattern (Antenna pattern (sidelobe)sidelobe) effecteffect Geolocation errorGeolocation error Pre-launch calibration offsetPre-launch calibration offset

No SI-traceable standardsNo SI-traceable standards

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ChallengesChallenges Corrections of known biases (last slide)Corrections of known biases (last slide) Metadata (sensor degradation, satellite Metadata (sensor degradation, satellite

maneuver, etc.), data QCmaneuver, etc.), data QC Impacts from both Impacts from both surfacesurface and atmosphere and atmosphere

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Data collectionData collectionAMSU L1B data (1998 – present)AMSU L1B data (1998 – present)

AMSU L2 data (2000 – present)AMSU L2 data (2000 – present)

ECMWF Interim (1998 – 2008)ECMWF Interim (1998 – 2008)

PATMOS-x cloud data (NOAA-15 & -18 2007 - 2009, soon to be PATMOS-x cloud data (NOAA-15 & -18 2007 - 2009, soon to be complete)complete)

MetadataMetadataMSPPS, legacy project logMSPPS, legacy project log

NOAA/NESDIS/OSDPD, operational collectionNOAA/NESDIS/OSDPD, operational collection

Asymmetry characterizationAsymmetry characterization

ProgressProgress (since April 2010)(since April 2010)

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AMSU-A TAMSU-A Tbb across scan asymmetry across scan asymmetry NOAA-18 Ascending TNOAA-18 Ascending Tbb

AMSU-A Asymmetry (1/3)AMSU-A Asymmetry (1/3)

Bia

s

Asymmetry

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Impact of TImpact of Tbb asymmetry on products asymmetry on products

AMSU-A Asymmetry (2/3)AMSU-A Asymmetry (2/3)

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Possible CausesPossible CausesReflector misalignmentReflector misalignmentBias in polarization vector orientationBias in polarization vector orientationSidelobe effectsSidelobe effectsAsymmetric atmosphere and surfaceAsymmetric atmosphere and surface

CharacterizationCharacterizationComparison of observation with CRTM simulationComparison of observation with CRTM simulationClear sky, over tropical and sub-tropical oceans (40N – Clear sky, over tropical and sub-tropical oceans (40N –

40S) 40S) Cloud screening approachesCloud screening approaches

AMSU L2 cloud productsAMSU L2 cloud productsPATMOS-x (AVHRR) cloud probabilityPATMOS-x (AVHRR) cloud probabilityERA Interim cloud probabilityERA Interim cloud probability

AMSU-A Asymmetry (3/3)AMSU-A Asymmetry (3/3)

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Asymmetry Characterization – L2 (1/5) Asymmetry Characterization – L2 (1/5) “Clear Sky” Definition“Clear Sky” Definition

L2 products: MSPPS AMSU-A Cloud Liquid Water (CLW) L2 products: MSPPS AMSU-A Cloud Liquid Water (CLW) and AMSU-B/MHS Ice Water Path (IWP)and AMSU-B/MHS Ice Water Path (IWP)

Clear-sky is identified when CLW = 0.0 and IWP = 0Clear-sky is identified when CLW = 0.0 and IWP = 0

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AMSU-A 1b raw count

Ta

Tb

Clear sky AMSU-A FOV determined by L2 productsOver tropical/subtropical oceans

ERA Interim T, q, O3 profiles; ERA interim SST, 10m U & V;

AMSU-A LZA, scan angle

Tb

Compare collocated Tb’s with same atmospheric condition for each beam position

CRTM

Asymmetry Characterization – L2 (2/5) Asymmetry Characterization – L2 (2/5) ProcedureProcedure

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OceansOceans

40 S – 40 N40 S – 40 N

Clear skyClear sky

Jan & Apr 2008Jan & Apr 2008

NOAA-18NOAA-18

ASC/DSC NodesASC/DSC Nodes

Small discrepancies Small discrepancies between ASC and DES between ASC and DES nodesnodes Channel-1 and -15 Asc Channel-1 and -15 Asc TTbb < Des T < Des Tbb

NOAA-18 is a PM NOAA-18 is a PM

satellite, Asc Tsatellite, Asc Tbb < Des T < Des Tbb

Asymmetry Characterization – L2 (3/5) Asymmetry Characterization – L2 (3/5) Observed TObserved Tbb

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ASC and DES ASC and DES

discrepancies mostly discrepancies mostly

towards limbtowards limb Ch-1 asymmetry is Ch-1 asymmetry is

basically linear, bias basically linear, bias

(-1K, 0.6K)(-1K, 0.6K) Ch-2 has double peak, Ch-2 has double peak,

bias (-0.9K, 0.6K)bias (-0.9K, 0.6K) Ch-3 has concave Ch-3 has concave

shape, bias (0K, 2.9K)shape, bias (0K, 2.9K) Ch-15 is basically Ch-15 is basically

linear, bias (-1.1K, 0.3K)linear, bias (-1.1K, 0.3K)

Asymmetry Characterization – L2 (4/5) Asymmetry Characterization – L2 (4/5) TTb b Bias and AsymmetryBias and Asymmetry

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All channels show All channels show

asymmetry seasonalityasymmetry seasonality Consistent asymmetry Consistent asymmetry

patternspatterns Ch-1 and -15 show the Ch-1 and -15 show the

largest seasonality, up largest seasonality, up

to 1Kto 1K Dec is upper bound Dec is upper bound

and Aug is lower bound and Aug is lower bound

for most channelsfor most channels

Asymmetry Characterization – L2 (5/5) Asymmetry Characterization – L2 (5/5) Asymmetry SeasonalityAsymmetry Seasonality

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PATMOS-x (AVHRR) cloud cover: 0.1 deg gridPATMOS-x (AVHRR) cloud cover: 0.1 deg grid Each AMSU-A FOV covers 14 to 100+ PATMOS-x pixels. Each AMSU-A FOV covers 14 to 100+ PATMOS-x pixels. Clear-sky is identified when every PATMOS-x pixel within the Clear-sky is identified when every PATMOS-x pixel within the

FOV is less than a certain cloud probability thresholdFOV is less than a certain cloud probability threshold Two thresholds are used: 10% and 50%Two thresholds are used: 10% and 50%

Cloud probability Cloud probability ≤≤ 50%, NOAA-18, 06/21/2008 50%, NOAA-18, 06/21/2008

ASC DES

Asymmetry Characterization – PATMOS-x (1/2) Asymmetry Characterization – PATMOS-x (1/2) “Clear Sky” Definition“Clear Sky” Definition

More cloud in DES than in ASC

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Asymmetry Characterization – PATMOS-x (2/2) Asymmetry Characterization – PATMOS-x (2/2) ResultsResults

Similarities to L2 approachSimilarities to L2 approach Observed ASC TObserved ASC Tbb < DES T < DES Tbb

Across scan asymmetry patternsAcross scan asymmetry patterns Seasonality, Dec upper bound and Aug lower bound.Seasonality, Dec upper bound and Aug lower bound.

DifferencesDifferences Asymmetry magnitudesAsymmetry magnitudes Less linearity in ch-1 and -15Less linearity in ch-1 and -15 Less agreement between ASC and DES TLess agreement between ASC and DES Tbb

Impact of cloud probability threshold:Impact of cloud probability threshold:

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Asymmetry Characterization – ERA (1/2) Asymmetry Characterization – ERA (1/2) “Clear Sky” Definition“Clear Sky” Definition

ERA Interim cloudsERA Interim clouds High cloud (> 6.38 km)High cloud (> 6.38 km)

Mid-cloud Mid-cloud

Low cloud (< 1.78 km)Low cloud (< 1.78 km)

Clear skyClear sky

When cloud cover probability is 0 at all three levelsWhen cloud cover probability is 0 at all three levels

Collocation of AMSU-A and ERA InterimCollocation of AMSU-A and ERA Interim ERA Interim has 0.703 deg spatial and 6-hr temporal ERA Interim has 0.703 deg spatial and 6-hr temporal

resolutions resolutions

Nearest neighbor in space and linear interpolation in timeNearest neighbor in space and linear interpolation in time

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Asymmetry Characterization – ERA (2/2) Asymmetry Characterization – ERA (2/2) ResultsResults

Similarity to L2 approachSimilarity to L2 approach Across scan asymmetry patternsAcross scan asymmetry patterns

DifferencesDifferences Observed ASC TObserved ASC Tbb > DES T > DES Tbb

Asymmetry magnitudesAsymmetry magnitudes

Less linearity in ch-1 and -15Less linearity in ch-1 and -15

Less agreement between ASC and DES TLess agreement between ASC and DES Tbb

Seasonality, Apr upper bound and Jul lower Seasonality, Apr upper bound and Jul lower

bound.bound.

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All show consistent All show consistent

across scan asymmetry across scan asymmetry

patternspatterns Different bias Different bias

magnitudes magnitudes

Asymmetry ComparisonAsymmetry ComparisonNOAA-18, 2008, ASCNOAA-18, 2008, ASC

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Next StepsNext Steps Correction of asymmetryCorrection of asymmetry

Better understanding of the various cloud data sets, Better understanding of the various cloud data sets, achieve better agreement in asymmetry pattern with achieve better agreement in asymmetry pattern with the different approachesthe different approaches

Stratify data by SST and wind to remove asymmetry Stratify data by SST and wind to remove asymmetry caused by heterogeneous surfacecaused by heterogeneous surface

Analyze reflector misalignment and polarization Analyze reflector misalignment and polarization issues and correct the corresponding biases by issues and correct the corresponding biases by adjusting scan angle adjusting scan angle

Inter-satellite calibrationInter-satellite calibrationSimultaneous Nadir Overpass (SNO) techniqueSimultaneous Nadir Overpass (SNO) technique

Double Difference Technique (DDT)Double Difference Technique (DDT)

Vicarious calibrationVicarious calibration

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SummarySummary AMSU-A TAMSU-A Tbb measurements suffer from many bias sources measurements suffer from many bias sources

such as warm target contamination and across scan such as warm target contamination and across scan

asymmetry.asymmetry.

CRTM and three cloud screening methods were used to CRTM and three cloud screening methods were used to

analyze the across scan asymmetry. They show similar Tanalyze the across scan asymmetry. They show similar Tbb

asymmetry patterns but different magnitudes.asymmetry patterns but different magnitudes.

Cloud screening method plays a critical role in characterizing Cloud screening method plays a critical role in characterizing

the across scan asymmetry of AMSU-A Tthe across scan asymmetry of AMSU-A Tbb. More study is . More study is

required torequired to achieve better agreement in asymmetry patterns achieve better agreement in asymmetry patterns

obtained with the different approaches.obtained with the different approaches.

SNO, DDT, and/or vicarious calibration will be used to perform SNO, DDT, and/or vicarious calibration will be used to perform

inter-satellite calibration in the near future.inter-satellite calibration in the near future.