1. 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

2. 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 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 Overview

3. 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. 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. 5Challenges  Corrections of known biases (last slide)  Metadata (sensor degradation, satellite maneuver, etc.), data QC  Impacts from both surface and atmosphere

6. 6  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 Progress (since April 2010)

7. 7  AMSU-A T b across scan asymmetry NOAA-18 Ascending T b NOAA-18 Ascending T b AMSU-A Asymmetry (1/3) Bias Asymmetry

8. 8  Impact of T b asymmetry on products AMSU-A Asymmetry (2/3)

9. 9  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 AMSU-A Asymmetry (3/3)

10. 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. 11 AMSU-A 1b raw count TaTa TbTb Clear sky AMSU-A FOV determined by L2 products Over tropical/subtropical oceans ERA Interim T, q, O 3 profiles; ERA interim SST, 10m U & V; AMSU-A LZA, scan angle TbTb Compare collocated T b ’s with same atmospheric condition for each beam position CRTM Asymmetry Characterization – L2 (2/5) Procedure

12. 12 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 T b < Des T b  NOAA-18 is a PM satellite, Asc T b < Des T b Asymmetry Characterization – L2 (3/5) Observed T b

13. 13  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) T b Bias and Asymmetry

14. 14  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

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

16. 16 Asymmetry Characterization – PATMOS-x (2/2) Results  Similarities to L2 approach  Observed ASC T b < DES T b  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 T b  Impact of cloud probability threshold:

17. 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 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. 18 Asymmetry Characterization – ERA (2/2) Results  Similarity to L2 approach  Across scan asymmetry patterns  Differences  Observed ASC T b > DES T b  Asymmetry magnitudes  Less linearity in ch-1 and -15  Less agreement between ASC and DES T b  Seasonality, Apr upper bound and Jul lower bound.

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

20. 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. 21 Summary  AMSU-A T b 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 T b asymmetry patterns but different magnitudes.  Cloud screening method plays a critical role in characterizing the across scan asymmetry of AMSU-A T b. 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.