Ongoing Activities toward the Improvement of the AMSR-E L2 Rain over Land Algorithm

1. Ongoing Activities toward the Improvement of the AMSR-E L2 Rain over Land Algorithm Ralph Ferraro, Nai-Yu Wang, Kaushik Gopalan and Arief Sudradjat NOAA/NESDIS, College Park, MD Cooperative Institute for Climate Studies, College Park, MD AMSR-E Science Team Meeting Arlington, VA

2. Outline • GPROF/land algorithm history/problems • Progress we are making • Warm season rain bias* • Convective/Stratiform separation • TB to RR conversions • *Land surface emissivity** • Land surface characterization** • Future plans *PMM Science Team **This project AMSR-E Science Team Meeting Arlington, VA

3. Examples of Problems… • Regional biases • Compared to PR • Compared to gauges • Unrealistic rain rate PDF’s in various regions? • “Strange” conditional rates • Underlying surface • Systematic biases. Why? • Unrealistic profiles in database • Bias towards tropical, ocean type systems • Not enough precip. regimes • Other “dirty laundry” • Surface snow • Inland water bodies Liu and Zipser AMSR-E Science Team Meeting Arlington, VA

4. Areas for Improvement • Convective classification (warm season bias)* • Diversify the land data base & better utilization of Bayesian retrieval • Land systems, all seasons* • Emissivity models • All useable measurements *Wang, N-Y., K. Gopalan, R. Ferraro, C. Liu, 2009: Version 7 of the TRMM2A12 Land Precipitation Algorithm, submitted, Geophysical Research Letter • Surface identification, Screening and removal of anomalous surfaces • “Modernize” for GPM era satellites • Commonality for GPM era satellites AMSR-E Science Team Meeting Arlington, VA

5. 7 years of PR and TMI match ups (post-boost period) a) 100% Convective b) 100% Stratiform AMSR-E Science Team Meeting Arlington, VA

6. Comparison of convective estimates from TMI and PR PR and New Version PR and Current Version AMSR-E Science Team Meeting Arlington, VA

7. TMI – PR global bias maps for TMI v6 and TMI regression (v7) AMSR-E Science Team Meeting Arlington, VA

8. Global RR Cumulative distributions for PR, TMI v6 and TMI v7 AMSR-E Science Team Meeting Arlington, VA

9. Storm over Argentina : Dec 2003 V6 RR V7 RR PR RR AMSR-E Science Team Meeting Arlington, VA

10. Scientific Need For Land Algorithms**From PMM Science Team Meeting, July 2008 • Improved surface characterization is urgently needed to advance the GPM-era precipitation over land algorithms • Algorithm scientists focusing on a wide range of topics, but not necessarily on land surface characterization • Land/winter season precipitation microphysics • Radiative transfer in precipitating atmospheres • Benefits/Utilization of high frequency measurements • However, much expertise is available through other programs • GEWEX • JCSDA/CRTM • CGMS – ITWG (+Emissivity Workshops), IPWG • However, it is not as simple as “plug and play” AMSR-E Science Team Meeting Arlington, VA

11. LSWG Objectives and Goals **From PMM Science Team Meeting, July 2008 • Assess the current state of land surface emissivity models/retrievals • Must be applicable to all GPM sensors • 6 – 200 GHz • What happens during active precipitation (if using static data base)? • Engage experts external to PMM • Establish methodology to accurately characterize the land surface for GPM core and constellations sensors • Must consider sensor and orbital characteristics • Frequency/FOV • Sensitivity to surface • Must be both static and dynamic in nature • Static – Land/Water/Terrain/ЄClimatology • Dynamic - snow cover, Tsfc, Є, changing water boundaries (e.g., Aral Sea) AMSR-E Science Team Meeting Arlington, VA

12. How we can use Єinformation Clear sky T/Q Profiles Precipitation Microphysical Information Surface Emissivity Surface Emissivity Atmosphere Emission scattering Emissivity Model Radiative Transfer Model Land Surface Type Simulated TB Observed TB AMSU, SSMIS Slide courtesy of N-Y. Wang AMSR-E Science Team Meeting Arlington, VA

13. Study Parameters • 12 Targets/9 types of surfaces • 1 Year: 1 July 06 – 30 June 07 • Assemble data sets: • Satellite • AMSR-E, SSMI, SSMIS, TMI, AMSU, WindSat • Ancillary satellite • ISCCP, PR/VIRS, CloudSat • Model • GDAS, LSM, JCSDA Emissivity • Participants generate emissivity “their way” but: • Must use only the data sets supplied • Make results freely accessible by others (post on web) • Results to be stratified by site, cloud mask, etc. AMSR-E Science Team Meeting Arlington, VA

14. Study Domain • A diverse set of targets were selected: • C3VP – 44 N, 80 W • Amazon(2) – 7 S, 70 W and 2 N, 55 W • Open Ocean(3) – 0 N, 150 W; 35 N, 30 W; 45 S, 35 W • Desert – 22 N, 29 E • SGP – 35 N, 97 W • Inland Water – 48 N, 87 W • SE US (HMT-E) - 34 N, 81 W • Wetland surface - 18 S, 57 W • Finland – 60 N, 25 E AMSR-E Science Team Meeting Arlington, VA

15. Study Web Pagehttp://cics.umd.edu/~rferraro/LSWG.html AMSR-E Science Team Meeting Arlington, VA

16. MIRS (1DVAR) Results – AMSR-E Results courtesy of S. Boukabara/W. Chen, NOAA/NESDIS • 6.9V: relatively small interannual • Variability EXCEPT: • Finland in winter • C3VP in winter • SGP due to vegetation/lack of it • Wetlands in fall • 89V: highly variable for most sites AMSR-E Science Team Meeting Arlington, VA

17. CICS - Amazon Target Clear Sky Cloudy AMSR-E Results courtesy of N-Y. Wang/K. Gopalan TMI • 37 GHz or less • Reasonable stability of Єover vegetated target • Cloud affects minimal • 89 GHz • Cloud and precipitation affects dramatic • Similar values during clear conditions AMSR-E Science Team Meeting Arlington, VA

18. Summary • Incremental progress made on GPROF-Land for TRMM V7 • Reduce warm season bias • Convective-Stratiform • TB85V to RR relationships • This version could be adopted for AMSR-E next version • TRMM version delivered and undergoing integration and testing • More work lies ahead on land surface characterization and its utilization • Larger algorithm overhaul for GPM-era AMSR-E Science Team Meeting Arlington, VA