A Combined Radar/Radiometer Retrieval for Precipitation

1. A Combined Radar/Radiometer Retrieval for Precipitation Christian Kummerow1, S. Joseph Munchak1,2 1 Dept. of Atmospheric ScienceColorado State University 2NASA/Goddard Space Flight Center IGARSS – Session 1.1 Vancouver, Canada 26 July, 2011

2. Existing Algorithms for TRMM • Radiometer-only • TMI 2A12, aka GPROF • Radar-only • PR 2A25 • Combined • 2B31 Biases against Ground Radar (1999-2004)‏ TMI PR COM (2B31)‏ Kwajalein -7.9% -13.7% -5.7% Melbourne, FL-8.2% +4.1%+21.3%

3. Precipitation Radar 2A25 Reflectivity (Z) profile Z-R, Z-k relationship Modify epsilon (rain DSD)‏ Surface reflection SRT reliable? N Y N Consistent with SRT PIA? Y Rain Profile

4. GPROF (TMI 2A12) Profile Database CRM (V6) Observed Tb (Brightness temperature)‏ Rain/No rain Bayesian matching Nonraining parameters Rain Parameters

5. Combined Algorithms Reflectivity profile Assumptions: rain DSD, ice density. Cloud water Modify assumptions Surface reflection Radiative Transfer N Observed Tbs Consistent with SRT PIA and Tbs? Y Rain Profile

6. Algorithm Philosophy • Build on existing single-sensor methods • Core is a radar profiling algorithm similar to 2A25 • Use internally consistent, interchangeable modules for scattering physics and radiative transfer • Improve upon previous combined algorithms • Identify key assumptions needed for microwave RT and use these as variable inputs to radar profiling algorithm • Minimize errors over large scenes to overcome beam filling and field-of-view overlap problems • Use all channels to maximize resolution and sensitivity to rain

7. Inversion Method • Use variational optimal estimation (OE) to minimize cost function over scene: retrieval parameter term observation term What is included in the retrieval parameter term?

8. Rain parameters: Precipitation ice Melting layer Cloud water Rain water Modeling Microwave Tbs requires knowledge of: Non-raining parameters: • Surface emission (SST, wind) • Water vapor • Cloud water

9. Retrieval of Non-Rain Parameters Adapted from Kummerow and Elsaesser (2008)‏

10. Retrieval of Precipitation Parameters Ice layer: contributes to scattering at 85 and 37 GHz Melting layer: strongly contributes to emission and radar attenuation Cloud water, water vapor: relatively weak sources of emission and attenuation Rain layer: contributes to emission and radar attenuation

11. What drives ice scattering at a given PR reflectivity? • Snow/graupel partitioning is fixed by height and rain type • Define retrieval parameter εICE to adjust exponential ice PSD: • D0=εICEaZb • where a and b are fixed by species and rain type Increase graupel fraction (density)‏ Increase IWP (Decrease D0)

12. Ice Retrieval

13. What drives emission/extinction in the rain layer at a given PR reflectivity? • Assume gamma distribution with shape parameter μ=3 • Define retrieval parameter εDSD to adjust rain DSD: • D0=εDSDaZb • where a and b are fixed by rain type

14. Rain DSD Retrieval

15. Cloud water: location vs. amount • Initial profiles: Cloud water is a fraction of rain water that depends on height and rain type • Define retrieval parameter εCLW as a multiplier for the total integrated amount of cloud water

16. Cloud Water Retrieval

17. What assumptions are necessary to model melting layer? Use reflectivity peak to determine melt density Use reflectivity profile to determine melt fraction Use same DSD assumption as rain

18. Algorithm Flow Non-rain parameter retrieval OE retrieval: cloud water/drizzle outside raining area OE retrieval: ice PSD OE retrieval: rain DSD, cloud water Retrieval Parameters: εDSD,εICE,εCLW

19. Back to the original question: Can a combined algorithm improve upon radar- or radiometer-only product biases in multiple locations?