1. Assessment of Tropical Rainfall Potential (TRaP) forecasts during the 2003-04 Australian tropical cyclone season Beth Ebert BMRC, Melbourne, Australia with thanks to Sheldon Kusselson, Mike Turk, Ralph Ferraro and Bob Kuligowski 2 nd IPWG Meeting, Monterey, 25-28 October 2004

2. TRaP - Tropical Rainfall Potential NESDIS nowcasts of rain in tropical cyclones Generation of TRaP: 1.Compute areal rain rates from passive microwave sensor (SSM/I, AMSU, or TRMM) 2.Using operational forecast cyclone track, advect rainfall for 24 h, assuming steady state storm structure 3.Analyst vets TRaP prior to public release TC Craig, 10 March 2003 DARWIN SSM/I "snapshot" TRaP

3. Validation of TRaP over U.S. for 2002 Atlantic hurricane season (Ferraro et al., 2004, Wea. Forecasting, submitted) SSM/I AMSU TRMM 42 TRaPs verified against Stage IV radar/gauge analyses at 4 km resolution. TRaP under-estimated rain rate, volume, max. TRaPs from TRMM performed best, closely followed by AMSU. TRaP outperformed Eta NWP model forecasts at 50 km resolution. Sensor (cases) Rain rate (TRaP / Stage IV) Rain volume (TRaP / Stage IV) TRaP maximum rain (mm d -1 ) Stage IV maximum rain (mm d -1 ) AMSU (11)0.590.5590.1351.8 SSM/I (16)0.710.65106.1345.0 TRMM (15)0.840.77140.5355.9

4. Atlantic vs. South Pacific hurricane rainfall Mean rain rate from TRMM TMI as a function of radial distance from storm center, 1998-2000 Lonfat et al., 2004, Mon. Wea. Rev. Atlantic South Pacific

5. 2003-2004 Australian tropical cyclones Validation strategies: maximum 24 h rain at landfall vs. rain gauge observations ±3h (±12 h) spatial rainfall distribution in 10 ° box vs. operational 0.25 ° gauge analysis ±3h contiguous rain area (CRA) bounded by 20 mm d -1 threshold vs. operational 0.25 ° gauge analysis ±3h (25) (9) (19) (29) (3)

6. Tropical Cyclone Fay (17-28 March 2004) TRaP too great on most days, especially near landfall Some extreme values for SSM/I and TRMM * Areal TRaP vs gauge observations not ideal but no radar data available landfall

7. Tropical Cyclone Fay (28 March 2004) Maximum 24 h rain (mm) Observed159.4 0100 UTC 28 March 2004 AMSU111.6 0233 UTC 28 March 2004 SSM/I478.1 1304 UTC 27 March 2004 TRMM251.0 0156 UTC 28 March 2004 AMSU SSM/ITRMM OBS

8. Maximum rain at landfall TRaP estimated maximum rain well for some TCs, overestimated for others AMSU less likely to overestimate Mean

9. * statistics for land grid boxes only Spatial validation - TC Fay (28 March 2004)

10. Aggregated results – all vs. vetted (checked by analyst) TRaPs Rain area and volume too small by ~50% POD for heavy rain is ~0.2-0.6, FAR is ~0.2-0.6 Vetted TRaPs perform better than all (unvetted + vetted) TRaPs

11. Aggregated results – sensor intercomparison SSM/I TRaPs had some large errors, AMSU had smallest errors AMSU TRaPs gave largest rain area AMSU TRaPs showed best performance, then TRMM, then SSM/I

12. CRA verification method (Ebert and McBride, 2000) Define entities using threshold (Contiguous Rain Areas) Location error determined by  pattern matching (minimum total squared error, maximum correlation, or maximum overlap)  external specification using best track data Verify properties of CRA (size, mean and maximum intensity, etc.) Error decomposition MSE total = MSE displacement + MSE volume + MSE pattern Version for pattern matching using correlation: (r=correlation, s=std.dev.) Observed X Forecast F...track errors...rain retrieval, no growth/decay...steady state rain structure Related to:

13. CRA validation TC Fay (0303 UTC 25 March 2004)

14. CRA validation TC Monty (2216 UTC 1 March 2004)

15. CRA validation results for vetted TRaPs Pattern error most important, followed by volume error, then displacement error 150 100 50 0 (km) (%) (%) (%)

16. Comparison to operational NWP Mesoscale model (mesoLAPS, 12 km resolution) TC-centered mesoscale model (TC-LAPS, 15 km resolution) 24 h rain forecasts for TC Monty, ~00 UTC 2 March 2004 Verification on 0.25° grid consistent with TRaP verification

17. Comparison to operational NWP NWP models overestimated rain area and volume Correlations comparable between TRaP and models Threat score best for TC-LAPS  Fairer comparison might use vetted TRaPs but not enough days in common TRaP

18. Comparison of Australian and US results (median values for vetted TRaPs) AustraliaUnited States Maximum rainfalltoo large by ~1/3~ 1/3 of observed Heavy rain area~ half of observed~ 2/3 of observed Heavy rain volume~ half of observed~ 2/3 of observed Error magnitude RMS error  R POD (heavy rain)~0.45~0.50 FAR (heavy rain)~0.30~0.25 Sensor intercomparison AMSU outperformed SSM/I, not enough TRMM to judge TRMM best, then AMSU, then SSM/I Comparison to NWPWorse in many respects Better in almost all respects

19. Reasons for differences AustraliaUnited States Reference dataRain gauge analysis, Stage IV radar-gauge analysis Spatial scale~25 km4 km Temporal matching± 3 h± 30 min NWP modelHigh resolution (12-15 km)Low resolution (50 km) Typical TC sizeBigger than averageSmaller than average Atmospheric moistureDrierMoister

20. CRA validation suggests... Location Error 18% Volume error 34% Pattern error 48% track forecasts satellite rain retrieval no growth or decay steady state rain structure sources of error related to assumptions in the TRaP formulation... Improve satellite rain algorithms Adjust for atmospheric moisture, shear Orographic enhancement Include storm rotation Statistical filter Improve tracks (multi-model NWP) that might be improved using a variety of strategies.

21. Living with uncertainty – Ensemble TRaP Perturb or vary: Cyclone track Parameters of microwave rain rate retrieval Satellite sensors included in the ensemble, including VIS/IR Sources of TC rain forecasts: R-CLIPER, NWP,... TC Monty, 00 UTC 2 March 2004 Ensemble of 27 TRaP forecasts (15 AMSU, 8 SSM/I, 4 TRMM) valid within ± 12 h Mean includes histogram transformation

22. Thank you!