A Global Rainfall Validation Strategy

1. A Global Rainfall Validation Strategy Wesley Berg, Christian Kummerow, and Tristan L’Ecuyer Colorado State University

2. All existing satellite rainfall retrieval algorithms are significantly under constrained. This means that global assumptions are made which may or may not be applicable to many regions and/or times.

3. Satellite Rainfall BiasesMean DJF Rainfall (1987 – 1996)

4. Satellite Rainfall Biases (Bias Adjusted TRMM Retrievals)

5. Regional IR (GPI) Biases (TRMM VIRS-PR)

6. Regional TRMM Biases(TMI-PR)

7. Regional differences in the structure and microphysical properties of rain systems occur as the result of differences between meteorological regimes.

8. East vs. West Pacific Stratiform/Convective Rain Profiles

9. East vs. West Pacific Rain Column Height Storm Height (km)

10. These regime-dependent changes in cloud structure and microphysical properties lead to systematic regional biases in the satellite retrieval algorithms due to global algorithm assumptions.

11. Algorithm Assumptions IR Retrievals • Relationship of cloud-top temperature to surface rain rate is constant Radiometer (ocean) • Freezing level is known • Shape of rain profile is independent of location • Horizontal inhomogeneity is constant Radiometer (land) • Relationship of ice aloft to surface rainfall is constant • All rain has a scattering signature (no warm rain) Radar • Drop Size Distribution

12. 0C 0C Surface Rainfall Surface Rainfall 0C 0C Surface Rainfall Surface Rainfall Passive Microwave Retrievals Column integrated water vs rainfall rate Freezing Level Rain Profile

13. Passive Microwave Retrievals Rain Profile

14. Passive Microwave RetrievalsLiquid Water Column Height (Freezing Level) TMI Freezing Level – PR Bright Band Height

15. Passive Microwave Retrievals Horizontal Inhomogeneity Beam Filling Correction Factor (January – March, 2000) Rainfall Total (mm) Beam Filling Factor

16. Precipitation Radar RetrievalsRadar Rainfall for 40 dBZ (Battan, 1973)

17. Precipitation Radar Retrievals Drop Size Distribution Epsilon Adjustment for Convective Rain (DJF 1997/98)

18. While it is possible to “tune” an algorithm for a specific region, without continuous monitoring by a network of ground-based observations time-dependent changes in cloud morphology associated with rainfall regime changes can still result in biased estimates.

19. Regional Validation

20. The determination of a “best” rainfall algorithm is highly dependent on both application as well as location.

21. Which is the Best Algorithm? • Depends on Application • Temporal sampling requirements • Spatial sampling requirements • Observations needed (e.g. latent heating) • Accuracy requirements (unbiased?) • Depends on Region • Land or Ocean? • Snow covered or desert? (no passive microwave) • High Latitude?

22. TRMM PR vs. TMI Coverage

23. With the proper algorithm framework, local observations should contribute to optimizing quality of regional satellite retrievals.

24. Satellite Observations Global Product Global Cloud Database Regional Observations Regional Product Regional Cloud Database Using Local Observations(with GPROF type algorithm)

25. High quality regional observations can be used to improve the global algorithm assumptions if the observations are properly grouped via satellite observables.

26. Satellite Observations Global Product Global Cloud Database Rain Classification High Quality Observations Regional Observations Regional Product Regional Cloud Database Using Local Observations(with GPROF type algorithm)

27. A Global Validation Scheme • Classify rain using satellite observables (e.g. horizontal and vertical reflectivity structure) • Use ground-based observations to determine mean and variability of satellite assumed values (such as DSD) for each rain class. • Export constrained values globally using satellite observations. • Use variability in constrained values to determine global uncertainties

28. Near-Surface Z  Y (km) Storm Top Height X (km) Reflectivity (dBZ) TOA  Z h Ratios : 6 km Height (km) Surface Z Slopes (0-2, 2-4, 4-6, and 6-8 km)  h  Z Height of Max Z Max Z Surface Z Near-Surface Z Reflectivity (dBZ) Rain Classification Scheme Mean and Max. Gradient Between Nearest Neighbors

29. Category I Category II Mean Height (km) Mean (dB)  (%) 0.85 0.397 1.01 0.494 0.71 0.141 0.76 0.139 1.34 0.424 1.37 0.412 PDF (ZDR) Reflectivity (dBZ) Easterly Westerly ZDR (dB) TRMM-LBA Easterly vs. Westerly Regimes

30. Summary • All existing satellite rainfall retrieval algorithms are significantly under constrained. This means that global assumptions are made which may or may not be applicable to many regions and/or times. • Changes in the structure and microphysics of rain systems between regions leads to systematic biases in the satellite retrieval algorithms. • The determination of a “best” rainfall algorithm is highly dependent on both application as well as location. • While it is possible to “tune” an algorithm for a specific region, without continuous monitoring by a network of ground-based observations time-dependent changes in cloud morphology such as those associated with El Niño can still result in biased estimates. • With the proper algorithm framework, local observations contribute to improving quality of regional satellite retrievals. They may also feed back into the global retrieval if they are of sufficient quality. • It appears possible to develop a methodology which constrains the algorithm assumptions (i.e. DSD) based on a detailed classification via the satellite observables.