Development of AMSU-A Fundamental CDR’s

Huan Meng1, Wenze Yang2, Ralph Ferraro1

1NOAA/NESDIS/STAR/CoRP/Satellite Climate Studies Branch2NOAA Corporate Institute for Climate and Satellites

Huan.Meng@noaa.gov

2

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

Record (CDR) program Goals:

Develop Advanced Microwave Sounding Unit-A and –B (AMSU-A/-B) and Microwave Humidity Sounder (MHS) FCDR’s for “window” and water vapor channelsAMSU-A: 23.8, 31.4, 50.3, 89.0 GHzAMSU-B/MHS: 89, 150/157; 183+1, 183+3, 183+7/190.3

GHzDevelop TCDR’s for hydrological products (rain, snow,

etc.) Source Data

NOAA-15,16,17,18,19 & MetOp-A L1B data

Overview

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

km at nadir; “mixed” polarizations 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 Biases Across scan asymmetryChanges over orbit (ASC/DSC)Changes over life of sensor

Warm target contamination (Zou et al., to be submitted)Orbital drift + Sun heatingInstrument (nonlinear) calibration error

Reflector emission Orbital decay Diurnal drift Antenna pattern (sidelobe) effect Geolocation error Pre-launch calibration offsetNo SI-traceable standards

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

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

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Data collectionAMSU L1B data (1998 – present)AMSU L2 data (2000 – present)ECMWF Interim (1998 – 2008)PATMOS-x cloud data (NOAA-15 & -18 2007 - 2009,

soon to be complete)

MetadataMSPPS, legacy project logNOAA/NESDIS/OSDPD, operational collection

Asymmetry characterization

Progress(since April 2010)

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AMSU-A Tb across scan asymmetryNOAA-18 Ascending Tb

AMSU-A Asymmetry (1/3)

Bia

s

Asymmetry

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Impact of Tb asymmetry on productsAMSU-A Asymmetry (2/3)

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Possible CausesReflector misalignmentBias in polarization vector orientationSidelobe effectsAsymmetric atmosphere and surface

CharacterizationComparison with CRTM simulationsClear sky, over tropical and sub-tropical oceans (40N –

40S) Cloud screening approaches

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

AMSU-A Asymmetry (3/3)

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

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

Clear-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) Procedure

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Oceans40 S – 40 NClear sky

Jan & Apr 2008NOAA-18

ASC/DSC Nodes

Small discrepancies between ASC and DES nodes Channel-1 and -15 Asc Tb < Des TbNOAA-18 is a PM satellite, Asc Tb < Des Tb

Asymmetry Characterization – L2 (3/5) Observed Tb

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ASC and DES discrepancies mostly towards limb Ch-1 asymmetry is basically linear, bias (-1K, 0.6K) Ch-2 has double peak, bias (-0.9K, 0.6K) Ch-3 has concave shape, bias (0K, 2.9K) Ch-15 is basically linear, bias (-1.1K, 0.3K)

Asymmetry Characterization – L2 (4/5) Tb Bias and Asymmetry

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All channels show asymmetry seasonality Consistent asymmetry patterns Ch-1 and -15 show the largest seasonality, up to 1K Dec is upper bound and Aug is lower bound for most channels

Asymmetry Characterization – L2 (5/5) Asymmetry Seasonality

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

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

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

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

More cloud in DES than in ASC

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

Similarities to L2 approach Observed ASC Tb < DES Tb Across scan asymmetry patterns Seasonality, Dec upper bound and Aug lower bound.

Differences Asymmetry magnitudes Less linearity in ch-1 and -15 Less agreement between ASC and DES Tb

Impact of cloud probability threshold:

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

ERA Interim clouds High cloud (> 6.38 km) Mid-cloud Low cloud (< 1.78 km)

Clear skyWhen cloud cover probability is 0 at all three levels

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

resolutions Nearest neighbor in space and linear interpolation in time

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Asymmetry Characterization – ERA (2/2) Results

Similarity to L2 approachAcross scan asymmetry patterns

Differences Observed ASC Tb > DES Tb

Asymmetry magnitudes Less linearity in ch-1 and -15 Less agreement between ASC and DES Tb

Seasonality, Apr upper bound and Jul lower bound.

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All show consistent across scan asymmetry patterns Different bias magnitudes

Asymmetry ComparisonNOAA-18, 2008, ASC

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Next Steps Correction of asymmetryBetter understanding of the various cloud data sets,

achieve better agreement in asymmetry pattern with the different approaches

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

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

Inter-satellite calibrationSNODouble difference techniqueVicarious calibration

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Summary AMSU-A Tb measurements suffer from many bias sources

such as warm target contamination and across scan asymmetry.

CRTM and three cloud screening methods were used to analyze the across scan asymmetry. They show similar Tb

asymmetry patterns but different magnitudes.

Cloud screening method plays a critical role in characterizing the across scan asymmetry of AMSU-A Tb. More study is required to achieve better agreement in asymmetry patterns obtained with the different approaches.

SNO, DDT, and/or vicarious calibration will be used to perform inter-satellite calibration in the near future.