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Development of AMSU-A Fundamental CDR’s

Development of AMSU-A Fundamental CDR’s. Huan Meng 1 , Wenze Yang 2 , Ralph Ferraro 1 1 NOAA/NESDIS/STAR/CoRP/Satellite Climate Studies Branch 2 NOAA Corporate Institute for Climate and Satellites Huan.Meng@noaa.gov. Overview. Background:

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Development of AMSU-A Fundamental CDR’s

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  1. Development of AMSU-A Fundamental CDR’s Huan Meng1, Wenze Yang2, Ralph Ferraro1 1NOAA/NESDIS/STAR/CoRP/Satellite Climate Studies Branch 2NOAA Corporate Institute for Climate and Satellites Huan.Meng@noaa.gov

  2. Overview • 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 channels • AMSU-A: 23.8, 31.4, 50.3, 89.0 GHz • AMSU-B/MHS: 89, 150/157; 183+1, 183+3, 183+7/190.3 GHz • Develop TCDR’s for hydrological products (rain, snow, etc.) • Source Data • NOAA-15,16,17,18,19 & MetOp-A L1B data

  3. 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

  4. AMSU-A SDR Biases • Across scan asymmetry • Changes over orbit (ASC/DSC) • Changes over life of sensor • Warm target contamination(Zou et al., to be submitted) • Orbital drift + Sun heating + Instrument nonlinear calibration error • Reflector emission • Orbital decay • Diurnal drift • Antenna pattern (sidelobe)effect • Geolocation error • Pre-launch calibration offset • No SI-traceable standards

  5. Challenges • Corrections of known biases (last slide) • Metadata (sensor degradation, satellite maneuver, etc.), data QC • Impacts from both surface and atmosphere

  6. Progress(since April 2010) • Data collection • AMSU 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) • Metadata • MSPPS, legacy project log • NOAA/NESDIS/OSDPD, operational collection • Asymmetry characterization

  7. AMSU-A Asymmetry (1/3) Bias Asymmetry • AMSU-A Tb across scan asymmetry NOAA-18 Ascending Tb

  8. AMSU-A Asymmetry (2/3) • Impact of Tb asymmetry on products

  9. AMSU-A Asymmetry (3/3) • Possible Causes • Reflector misalignment • Bias in polarization vector orientation • Sidelobe effects • Asymmetric atmosphere and surface • Characterization • Comparison of observation with CRTM simulation • Clear sky, over tropical and sub-tropical oceans (40N – 40S) • Cloud screening approaches • AMSU L2 cloud products • PATMOS-x (AVHRR) cloud probability • ERA Interim cloud probability

  10. 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

  11. Asymmetry Characterization – L2 (2/5) Procedure Clear sky AMSU-A FOV determined by L2 products Over tropical/subtropical oceans ERA Interim T, q, O3 profiles; ERA interim SST, 10m U & V; AMSU-A LZA, scan angle AMSU-A 1b raw count Ta CRTM Tb Tb Compare collocated Tb’s with same atmospheric condition for each beam position

  12. Asymmetry Characterization – L2 (3/5) Observed Tb Oceans 40 S – 40 N Clear sky Jan & Apr 2008 NOAA-18 ASC/DSC Nodes • Small discrepancies between ASC and DES nodes • Channel-1 and -15 Asc Tb < Des Tb • NOAA-18 is a PM satellite, Asc Tb < Des Tb

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

  14. Asymmetry Characterization – L2 (5/5) Asymmetry Seasonality • 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

  15. Asymmetry Characterization – PATMOS-x (1/2) “Clear Sky” Definition • 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, 06/21/2008 DES ASC More cloud in DES than in ASC

  16. 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:

  17. Asymmetry Characterization – ERA (1/2) “Clear Sky” Definition • ERA Interim clouds • High cloud (> 6.38 km) • Mid-cloud • Low cloud (< 1.78 km) • Clear sky When 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

  18. Asymmetry Characterization – ERA (2/2) Results • Similarity to L2 approach • Across 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.

  19. Asymmetry Comparison NOAA-18, 2008, ASC • All show consistent across scan asymmetry patterns • Different bias magnitudes

  20. Next Steps Correction of asymmetry Better 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 calibration Simultaneous Nadir Overpass (SNO) technique Double Difference Technique (DDT) Vicarious calibration

  21. 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 toachieve 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.

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