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A Combined Radar/Radiometer Retrieval for Precipitation  IGARSS – Session 1.1 Vancouver, Canada 26 July, 2011 Christian Kummerow 1 , S. Joseph Munchak 1,2 1  Dept. of Atmospheric Science Colorado State University 2 NASA/Goddard Space Flight Center
Existing Algorithms for TRMM ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],  TMI PR COM (2B31)‏ Kwajalein  7.9%  13.7%    5.7% Melbourne, FL  8.2%  +4.1% +21.3% Biases against Ground Radar (1999-2004)‏
Precipitation Radar 2A25 Reflectivity (Z) profile Z-R, Z-k relationship Consistent with SRT PIA?  Modify epsilon  (rain DSD)‏ SRT reliable? Rain Profile N N Y Y Surface reflection
GPROF (TMI 2A12) Observed Tb  (Brightness temperature)‏ Profile Database CRM (V6) Bayesian matching Rain/No rain Nonraining parameters Rain Parameters
Combined Algorithms Reflectivity profile Assumptions: rain DSD, ice density. Cloud water Consistent with SRT PIA and Tbs? Modify assumptions Radiative Transfer Rain Profile N Y Surface reflection Observed Tbs
Algorithm Philosophy ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Inversion Method ,[object Object],observation term retrieval parameter term What is included in the retrieval parameter term?
Modeling Microwave Tbs  requires knowledge of: ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Retrieval of Non-Rain Parameters Adapted from Kummerow and Elsaesser (2008)‏
Retrieval of Precipitation Parameters Ice layer: contributes to scattering at 85 and 37 GHz Melting layer: strongly contributes to emission and radar attenuation Rain layer: contributes to emission and radar attenuation Cloud water, water vapor: relatively weak sources of emission and attenuation
What drives ice scattering at a given PR reflectivity? Increase IWP (Decrease D 0 )  Increase graupel fraction (density)‏ ,[object Object],[object Object],[object Object],[object Object]
Ice Retrieval
What drives emission/extinction in the rain layer at a given PR reflectivity? ,[object Object],[object Object],[object Object],[object Object]
Rain DSD Retrieval
Cloud water: location vs. amount ,[object Object],[object Object]
Cloud Water Retrieval
What assumptions are necessary to model melting layer? ,[object Object],[object Object],[object Object]
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: ε DSD ,ε ICE ,ε CLW
Back to the original question: Can a combined algorithm improve upon radar- or radiometer-only product biases in multiple locations?

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Kummerow.1.1B.ppt

  • 1. A Combined Radar/Radiometer Retrieval for Precipitation IGARSS – Session 1.1 Vancouver, Canada 26 July, 2011 Christian Kummerow 1 , S. Joseph Munchak 1,2 1 Dept. of Atmospheric Science Colorado State University 2 NASA/Goddard Space Flight Center
  • 2.
  • 3. Precipitation Radar 2A25 Reflectivity (Z) profile Z-R, Z-k relationship Consistent with SRT PIA? Modify epsilon (rain DSD)‏ SRT reliable? Rain Profile N N Y Y Surface reflection
  • 4. GPROF (TMI 2A12) Observed Tb (Brightness temperature)‏ Profile Database CRM (V6) Bayesian matching Rain/No rain Nonraining parameters Rain Parameters
  • 5. Combined Algorithms Reflectivity profile Assumptions: rain DSD, ice density. Cloud water Consistent with SRT PIA and Tbs? Modify assumptions Radiative Transfer Rain Profile N Y Surface reflection Observed Tbs
  • 6.
  • 7.
  • 8.
  • 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 Rain layer: contributes to emission and radar attenuation Cloud water, water vapor: relatively weak sources of emission and attenuation
  • 11.
  • 13.
  • 15.
  • 17.
  • 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: ε DSD ,ε ICE ,ε CLW
  • 19. Back to the original question: Can a combined algorithm improve upon radar- or radiometer-only product biases in multiple locations?

Editor's Notes

  1. Rephrase as problem, “what affects Tbs” and show (with TMI footprint) how non-rain and rain parameters both contribute
  2. Simplify header, why is this important
  3. header
  4. Take out text (add slide after example retrieval), remove “OE”
  5. Shorten question