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Validation of Satellite-Derived Rainfall Estimates and

Validation of Satellite-Derived Rainfall Estimates and Numerical Model Forecasts of Precipitation over the US. John Janowiak Climate Prediction Center/NCEP/NWS. 2 nd Int’l Precipitation Working Group - October 26, 2004. Work is modeled after the pioneering effort of

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Validation of Satellite-Derived Rainfall Estimates and

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  1. Validation of Satellite-Derived Rainfall Estimates and Numerical Model Forecasts of Precipitation over the US John Janowiak Climate Prediction Center/NCEP/NWS 2nd Int’l Precipitation Working Group - October 26, 2004

  2. Work is modeled after the pioneering effort of Dr. Beth Ebert (BMRC/Australian BOM) www.bom.gov.au/bmrc/wefor/staff/eee/SatRainVal/dailyval_dev.html U.S. Validation at: www.cpc.ncep.noaa.gov/products/janowiak/us_web.shtml

  3. www.cpc.ncep.noaa.gov/products/janowiak/us_web.shtml

  4. www.cpc.ncep.noaa.gov/products/janowiak/us_web.shtml

  5. Validation Data Set Typical Station Distribution • - 7000+ station reports daily • 06Z – 06Z accumulation period • Data analyzed using a Cressman-type • scheme • Error characteristics of validation data are • NOT known • - Validation area matched for all estimates • (if missing in one, made missing in all)

  6. Validation Results

  7. Cold Season Precipitation Amt. (Jan 2004)

  8. Cold Season Precipitation Diff. (Jan 2004)

  9. Warm Season Precipitation Amt. (Jun 2004)

  10. Warm Season Precipitation Diff. (Jun 2004)

  11. Validation Data Set Typical Station Distribution

  12. CPC gauge analysis ( Aug 2003) CMORPH analysis ( Aug 2003) CMORPH with evap. adjustment

  13. Bias Ratio (areal coverage)

  14. west Bias Ratio (areal coverage) east

  15. BIAS Ratio (estimated mean / gauge mean)

  16. west east BIAS Ratio (estimated mean / gauge mean)

  17. Mean precip. for entire US (not to scale)

  18. Contribution to June 2004 Total Rainfall by Daily Rainfall Amount Heaviest 10% of daily rainfall events

  19. CONCLUSIONS 1. Merging PMW & IR estimates provides more accurate estimates of precipitation than the separate components can

  20. CONCLUSIONS 1. Merging PMW & IR estimates provides more accurate estimates of precipitation than the separate components can 2. Two major systematic biases are apparent in the satellite estimates: a. OVERestimation over snow-covered regions b. OVERestimation in semi-arid regions during the warm season

  21. CONCLUSIONS 1. Merging PMW & IR estimates provides more accurate estimates of precipitation than the separate components can 2. Two major systematic biases are apparent in the satellite estimates: a. OVERestimation over snow-covered regions b. OVERestimation in semi-arid regions during the warm season 3. NWP forecasts generally outperform blended satellite estimates and radar during the winter season over the U.S.

  22. The End

  23. Effects of Interpolating the Data

  24. Probability of Detection/False Alarm Ratio POD FAR

  25. Probability of Detection/False Alarm Ratio east POD west FAR

  26. Probability of Detection/False Alarm Ratio east POD west FAR

  27. Probability of Detection/False Alarm Ratio July 2004 POD FAR

  28. Probability of Detection/False Alarm Ratio July 2004 POD FAR January 2004

  29. CMORPH vs. gauge over ‘NAME*’ zones *North American Monsoon Experiment (2004)

  30. CPC gauge analysis ( Aug 2003) CMORPH analysis ( Aug 2003)

  31. CMORPH with RH adjustment vs. gauge over ‘NAME’ zones

  32. Statistics over 9 NAME Zones Evap. adjusted Evap. adjusted

  33. Distribution of Daily Precipitation Amounts for June 2004 45 50 55 60 65 70 75 80 85 90 >90

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