Validation of Satellite Precipitation Estimates for Weather and Hydrological Applications Beth Ebert BMRC, Melbourne, Australia 3 rd IPWG Workshop / 3.

Validation of Satellite Precipitation Estimates for Weather and Hydrological

Applications

Beth Ebert

BMRC, Melbourne, Australia

3rd IPWG Workshop / 3rd APSATS, 23 October 2006, Melbourne

Satellite precipitation estimates -- what do we especially want to get right?

Climatologists - mean bias

NWP data assimilation (physical initialization) - rain location and type

Hydrologists - rain volume

Forecasters and emergency managers - rain location and maximum intensity

Short-term precipitation estimates• High spatial and temporal resolution desirable

• Dynamic range required

• Motion may be important for nowcasts

• Can live with some bias in the estimates if it's not too great

• Verification data need not be quite as accurate as for climate verification

• Land-based rainfall generally of greater interest than ocean-based

Some truths about "truth" data

• No existing measurement system adequately captures the high spatial and temporal variability of rainfall.

• Errors in validation data artificially inflate errors in satellite precipitation estimates

Rain gauge observations

Advantages DisadvantagesTrue rain measurements May be unrepresentative of

aerial valueVerification results biased

toward regions with high gauge density

Most obs made once daily

Radar dataAdvantages DisadvantagesExcellent spatial and Beamfilling, attenuation,

temporal resolution overshoot, clutter, etc.Limited spatial extent

TRMM PR

Rain gauge analysesAdvantages DisadvantagesGrid-scale quantities Smoothes actual rainfall Overcomes uneven values

distribution of raingauges

Verification strategy for satellite precipitation estimates

Use (gauge-corrected) radar data for local instantaneous or very short-term estimates

Use gauge or radar-gauge analysis for larger spatial and/or temporal estimates

Spatial verification methods

• Visual ("eyeball") verification• Continuous statistics (RMS, correlation, bias, etc)• Categorical statistics (POD, FAR, etc.)

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

• Scale decomposition methods • Entity-based methods

"standard"

"scientific" or "diagnostic"

Step 1: Visual ("eyeball") verificationVisually compare maps of satellite estimates and

observations

Advantage: "A picture tells a thousand words…"

Disadvantages: Labor intensive, not quantitative, subjective

Verifies this attribute?LocationSizeShapeMean valueMaximum valueSpatial variability

Rozumalski, 2000

Time series of error statistics

24-hr rainfall from NRL Experimental Geostationary algorithm validated against Australian operational daily rain gauge analysis

0.25° grid boxes, tropics only

Continuous statistics

Mean absolute error

||1

1i

N

ii OF

NMAE

Measures average magnitude of error

Root mean square error2

1

)(1

i

N

ii OF

NRMSE

Measures error magnitude, with large errors having a greater impact than in the MAE

)(1

1i

N

ii OF

NMean Error

Mean error (bias) Measures average error

Correlation coefficient

22 )()(

)( )(

OOFF

OOFFr

Measures correspondence between estimated spatial distribution and observed spatial distribution, independent of mean bias

Estimated yes no

yes hits misses

no false correctalarms negativesO

bser

ved

Estimated Observed

Falsealarms

Hits

Misses

Correct negatives

Categorical statistics

Categorical statistics

Probability of Detectionmisseshits

hitsPOD

False Alarm Ratioalarmsfalsehits

alarmsfalseFAR

Threat score (critical success index)

alarmsfalsemisseshits

hitsCSITS

Equitable threat score

random

random

hitsalarmsfalsemisseshits

hitshitsETS

Bias score misseshits

alarmsfalsehitsBIAS

Real-time verification example24-hr rainfall from NRL Experimental Geostationary algorithm

Web links for satellite precipitation validation

• IPWG validation of daily rainfall estimates over many regions - http://www.bom.gov.au/bmrc/SatRainVal/validation-intercomparison.html

• Climate Rainfall Data Center (monthly validation, global) - http://rain.atmos.colostate.edu/CRDC/

• Program for the Evaluation of High Resolution Precipitation Products - http://essic.umd.edu/%7Emsapiano/PEHRPP/

• Validation of 6-hourly and daily Hydro-Estimator and other geostationary estimates over US - http://www.orbit.nesdis.noaa.gov/smcd/emb/ff/validation/validation.html

• TOVAS validation of daily TRMM-based estimates over US - http://disc2.nascom.nasa.gov/Giovanni/tovas/rain.ipwg.shtml

• Forecast verification – Issues, Methods, and FAQ - http://www.bom.gov.au/bmrc/wefor/staff/eee/verif/verif_web_page.html