Validation of Satellite-Derived Rainfall Estimates and Numerical Model Forecasts of Precipitation...

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Validation of Satellite-Derived Rainfall Estimates andNumerical Model Forecasts of Precipitation over the US

John Janowiak Climate Prediction Center/NCEP/NWS

2nd Int’l Precipitation Working Group - October 26, 2004

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

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

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

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

Validation Results

Cold Season Precipitation Amt. (Jan 2004)

Cold Season Precipitation Diff. (Jan 2004)

Warm Season Precipitation Amt. (Jun 2004)

Warm Season Precipitation Diff. (Jun 2004)

Validation Data Set

Typical Station Distribution

CPC gauge analysis ( Aug 2003)CPC gauge analysis ( Aug 2003)

CMORPH analysis ( Aug 2003)CMORPH analysis ( Aug 2003)

CMORPH with evap. adjustmentCMORPH with evap. adjustment

Bias Ratio (areal coverage)

Bias Ratio (areal coverage)

west

east

BIAS Ratio (estimated mean / gauge mean)

BIAS Ratio (estimated mean / gauge mean)

west

east

Mean precip. for entire US (not to scale)

Contribution to June 2004 Total Rainfall by Daily Rainfall Amount

Heaviest 10% of daily rainfall events

CONCLUSIONS

1. Merging PMW & IR estimates provides more accurate estimates ofprecipitation than the separate components can

CONCLUSIONS

1. Merging PMW & IR estimates provides more accurate estimates ofprecipitation than the separate components can

2. Two major systematic biases are apparent in the satellite estimates:a. OVERestimation over snow-covered regionsb. OVERestimation in semi-arid regions during the warm season

CONCLUSIONS

1. Merging PMW & IR estimates provides more accurate estimates ofprecipitation than the separate components can

2. Two major systematic biases are apparent in the satellite estimates:a. OVERestimation over snow-covered regionsb. 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.

Effects of Interpolating the Data

POD

FAR

Probability of Detection/False Alarm Ratio

POD

FAR

east

west

Probability of Detection/False Alarm Ratio

POD

FAR

east

west

Probability of Detection/False Alarm Ratio

POD

FAR

Probability of Detection/False Alarm RatioJuly 2004

POD

FAR

Probability of Detection/False Alarm RatioJuly 2004

January 2004

CMORPH vs. gauge over ‘NAME*’ zones

*North American Monsoon Experiment (2004)

CPC gauge analysis ( Aug 2003)CPC gauge analysis ( Aug 2003)

CMORPH analysis ( Aug 2003)CMORPH analysis ( Aug 2003)

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

Statistics over 9 NAME Zones

Evap. adjusted

Evap. adjusted

Distribution of Daily Precipitation Amounts for June 2004

45 50 55 60 65 70 75 80 85 90 >90

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

Bias Ratio (areal coverage)

Bias Ratio (areal coverage)

west

east

BIAS Ratio (mean radar/ mean gauge)

BIAS Ratio (mean radar/ mean gauge)

west

east