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 Biases Mean 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. Passive Microwave Retrievals Column integrated water vs rainfall rate Freezing Level Rain Profile Surface Rainfall 0C0C 0C0C 0C0C 0C0C

13. Passive Microwave Retrievals Rain Profile

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

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

16. Precipitation Radar Retrievals Radar Rainfall for 40 dBZ (Battan, 1973) LocationRainfall rate [mm/hr] Canada48.6 Hawaii112.6 Midwest50.2 Australia10.2 Washington, DC54.0 Massachusetts254.0 Moscow40.0 Poon, India43.0 France24.2 Franklin, NC76.7

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. 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? Which is the Best Algorithm?

22. TRMM PR vs. TMI Coverage

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

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

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

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

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

29. TRMM-LBA Easterly vs. Westerly Regimes Category I Category II Mean Reflectivity (dBZ) Height (km) ZDR (dB) PDF (ZDR) 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 Easterly Westerly

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.