1. 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

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

6. Validation Data Set - 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) Typical Station Distribution

7. Validation Results

12. Cold Season Precipitation Amt. (Jan 2004)

13. Cold Season Precipitation Diff. (Jan 2004)

14. Warm Season Precipitation Amt. (Jun 2004)

15. Warm Season Precipitation Diff. (Jun 2004)

17. Validation Data Set Typical Station Distribution

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

19. Bias Ratio (areal coverage)

20. west east

21. BIAS Ratio (estimated mean / gauge mean)

22. west east

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

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

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

28. 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

29. 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.

30. The End

31. Effects of Interpolating the Data

38. POD FAR Probability of Detection/False Alarm Ratio

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

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

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

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

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

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

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

49. Statistics over 9 NAME Zones Evap. adjusted

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

51. Distribution of Daily Precipitation Amounts for Jan 1-22, 2004

52. Bias Ratio (areal coverage)

53. west east

54. BIAS Ratio (mean radar/ mean gauge)

55. west east