Verifying Precipitation Events Using Composite Statistics

1. Verifying Precipitation Events Using Composite Statistics Jason Nachamkin Naval Research Laboratory, Monterey, CA

2. Looking for Tigers A region of heavy precipitation exists… • Does the estimate resemble the observations? • Is it even close (heavy precipitation within a radius of the estimate)? • To what degree is the estimate accurate? • Is the estimate more/less accurate under certain conditions?

3. Composite Verification Method • Identify events of interest in the forecasts (estimates) • Rainfall greater than 25 mm • Event contains between 100 and 1000 grid points • Define a kernel and collect coordinated samples • Square box • 31x31 grid points (155x155 km for 5 km grid) • Compare forecast PDF to observed PDF • Repeat process for observed events

4. Collecting the Samples Estimated event Independent Observations x Event center Collection kernel

5. Australian Precipitation Study • All 24-hour precipitation estimates • 2 June – 20 Aug. 2003 • 1 – 29 Feb. 2004 • Bureau of Meteorology Research Centre (Beth Ebert) • 0.25 deg rain gauge analysis (land) • 1000 stns (Feb), 5000, JJA • NRL Blended Satellite Algorithm (Joe Turk) • SSM/I, TRMM, AMSU-B + GOES, Meteosat • Dynamic, statistically-based adjustment • 0.25 deg grid • Data interpolated to 5 km Lambert Conformal COAMPS grid

6. Jun-Aug 2003 ETS/Bias All precipitation B=SAT/BMRC • Severe underestimation by satellite algorithm • Very few events sampled

7. Kernel Grid-Average Precipitation Average rain (mm) given an event was observed by BMRC Average rain (mm) given an event was observed by SAT BMRC-shade SAT-contour N=13 N=5 • Severe underestimation of inland events by satellite algorithm. • Satellite estimates were better near the coast.

8. Statistics From Single Events Max SAT Max BMRC Sample Integrated SAT Sample Integrated BMRC

9. Event Detection Frequencies JJA SAT more BMRC more • Several BMRC-observed events almost completely missed • Very few events sampled

10. 24 July 2003 Precipitation 24-hr BMRC Rain (mm) 24-hr SAT Rain (mm) 128 120 112 104 96 88 80 72 64 56 48 40 32 24 16 8 0 • “False alarm” along east coast • “Missed event” in southern interior

11. Feb 2004 ETS/Bias All precipitation B=SAT/BMRC • Severe overestimation of intense rainfall by satellite algorithm • Better areal coverage at lower intensities

12. Kernel Grid-Average Precipitation Average rain (mm) given an event was observed by BMRC Average rain (mm) given an event was observed by SAT BMRC-shade SAT-contour N=123 N=148 • Satellite much better at correctly placing events. • Satellite precipitation too heavy near event center. • Some “false alarms” in satellite estimates.

13. Event Detection Frequencies Feb SAT more BMRC more • When event in BMRC, satellite underestimates grid total and overestimates grid maximum. • False alarms contribute to satellite overestimation in the satellite event composite.

14. Daily Forecast Frequencies

15. 2 Feb 2004 Precipitation 24-hr BMRC Rain (mm) 24-hr SAT Rain (mm) 128 120 112 104 96 88 80 72 64 56 48 40 32 24 16 8 0 • General precipitation areas are similar. • Satellite contains greater detail, much higher maxima. • Possible false alarm in southeast.

16. Conclusions • During June-August, the satellite estimates often missed events except near the coast. • During February, the satellite performed better though often overestimated precipitation maxima. • “False alarms” and “missed events”, where the satellite and BMRC estimates differed significantly, were common. • Separate statistics from 24-hour model forecasts over the US were better than the satellite estimates.