1. VALIDATION OF HIGH RESOLUTION SATELLITE-DERIVED RAINFALL ESTIMATES AND OPERATIONAL MESOSCALE MODELS FORECASTS OF PRECIPITATION OVER SOUTHERN EUROPE 1st PEHRPP Workshop: Geneva (Switzerland), 3-5 December 2007 Laura Bertolani, Alessandro Perotto and Raffaele Salerno Epson Meteo Centre (EMC), Milan, ITALY Laura Bertolani, Alessandro Perotto and Raffaele Salerno Epson Meteo Centre (EMC), Milan, ITALY

2. MOTIVATION To evaluate the capabilities of the mesoscale models operationally run at Epson Meteo Centre in predicting precipitation on a daily basis, over different seasons, under various weather regimes, in comparison with satellite-derived estimates over the European domain by means of near real-time products.

3. Verification statistics used. Brief overview on recent past verification efforts: assessment of short-term QPFs from EMC-GCM and comparison with CMORPH precipitation estimates over Mid Latitudes and Tropics (March 2004- February 2005). The new and ongoing operational activity: assessment of QPFs from 2 mesoscale models, and comparison with 3 high resolution satellite derived precipitation estimates over Southern Europe; verification of QPFs from 2 high resolution mesoscale models against TRMM 3b42_v6 over Italy. Summary and remarks. OUTLINE OF TALK

4. hits misses false alarms correct negatives observationforecast/estimation unskilled forecast Continuous Verification Statistics: measure the accuracy of predicted or estimated rain amount. Mean Error (bias), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Correlation Coefficient. Categorical Verification Statistics: measure the accuracy of predicted or estimated rain occurence. BIASFARETSPOD HK PC Bias Score, Proportion Correct (PC), Probability of Detection (POD), False Alarm Ratio (FAR), Equitable Threat Score (ETS), Hansen and Kuipers Score (HK). WGNE HIGHLY RECOMMENDED STATISTICS

5. EMC-GLOBAL MODEL (1°x1°) and CMORPH (0.5°X0.5°) against CPC daily rain gauge analysis - Continuous Statistics CMORPH underestimates precipitation in cold and cool periods, while slightly overestimating it in the warm season; the model overpredicts rainfall all year long. CMORPH outperforms model forecasts in detecting rainfall amount in all seasons, except in winter. CMORPH is better correlated to observation than model output during the summer, not so much in the winter. Model d1 model d2 model d3 cmorph persistence gauge analysis Europe (36°-56°N; 10°W-25°E) Rain Rate RMS error Correlation India (5°-26°N; 70°-90°E) Rain Rate RMS error Correlation

6. MAMJJASONDJF ETS THE GLOBAL MODEL AND CMORPH HAD AN OPPOSITE BEHAVIOR THE MODEL PERFORMED BEST WHEN STRATIFORM PRECIPITATION PREVAILED; THE SATELLITE PRODUCT PERFORMED BEST WHEN CONVECTIVE RAINFALL PREVAILED. THE MODEL OUTPERFORMED CMORPH DURING MID-LATITUDE COOL AND COLD SEASONS MAMJJA SON DJF BIAS score EMC-GLOBAL MODEL (1°x1°) and CMORPH (0.5°X0.5°) against CPC daily rain gauge analysis - Categorical Statistics OVER EUROPE model d1 model d2 model d3 cmorph persistence

7.  1 gauge  2 gauges  3 gauges  4 gauges MESOSCALE MODELS WRF-NMM v2.2 32 km horizontal resolution, 38 vertical levels Kain Fritsch convection scheme Noah LSM (4 layers) Init. and b.c. data: 12z GFS T382L64 RSM 50 km horizontal resolution, 28 vertical levels Simplified Arakawa Schubert convection scheme OSU LSM (2 layers), homogeneous b.c. Init. and b.c. data: 12z EMC-GCM T126L28 OBSERVATION NOAA CPC Daily Rain Gauge Analysis 0.5°x0.5° lat/long, daily (18z-18z over Europe) SATELLITE ESTIMATES NOAA CPC CMORPH, NASA TRMM 3b42_v6 NASA TRMM 3b42RT 0.25°x0.25° lat/long, 3 hourly EMC MESOSCALE MODELS and SATELLITE PRODUCTS: EUROPE DOMAIN 36°-49°N; 10°W-30°E PERIOD Satellite products: September 2006 – October 2007 Models forecasts: June –October 2007 All data remapped to 0.5°X0.5° lat/long and accumulated to 18z-18z daily values (models = + 30 hrs). Statistics for land points only, and for grid boxes with at least 1 rain gauge inside.

8. SEASONAL MEAN (mm/d): fall 2006 - spring 2007 gauge analysistrmm 3b42RTtrmm 3b42_V6cmorph SON DJF MAM cmorph underestimates precipitation, especially during the cold season, trmm 3b42_v6 shows a better agreement with gauge analysis

9. Mean Error (mm/d): fall 2006 – spring 2007 trmm 3b42RTtrmm 3b42_V6cmorph SON DJF MAM

10. SEASONAL MEAN (mm/d): summer & fall 2007 JJA gauge analysistrmm 3b42RTtrmm 3b42_V6cmorph WRF RSM SO satellite-derived products and WRF in good agreement with gauge analysis, RSM overpredicts rainfall. gauge analysistrmm 3b42RTtrmm 3b42_V6cmorph WRFRSM

11. Mean Error (mm/d): summer & fall 2007 RSM d1 JJA trmm 3b42RTtrmm 3b42_V6cmorph WRF RSM SO_07 trmm 3b42RTtrmm 3b42_V6cmorph WRF RSM

12. Continuous Statistics – 10 day running mean cmorph 3b42 3b42RT WRF RSM 3b42RT and (especially) cmorph biased low during cold and cool seasons, biased slightly high in the summer. 3b42 has generally a stable trend. 3b42 and cmorph show a similar performance (a bit higher for 3b42 in the warm period, for cmorph in the cold season) and outperform 3b42RT. Mean Error RMSE Correlation Coefficient

13. Categorical Statistics: light vs. heavy precipitation Accuracy decreases with the highest thresholds both for satellite estimates and models forecasts. 3b42 outperforms the other products. WRF strongly outperforms RSM and outperforms satellites estimates during Fall for the lightest thresholds. SON 06 DJFJJAMAM SO 07 SON 06 DJF JJA MAM SO 07 cmorph trmm 3b42 trmm 3b42RT WRF RSM Winter  satellite-derived products underestimate rain occurrence (3b42 overestimates the heaviest events). Cool seasons  TRMMs and WRF overestimate the heaviest events, cmorph underestimates almost all thresholds. Summer  satellite derived products and WRF overestimate the heaviest rainfall events. BIAS score ETS

14. WRF-NMM v2.2 8 km horizontal resolution, 38 vertical levels Kain Fritsch convection scheme Noah LSM (4 layers) Init. and b.c. data: GFS T382L64 at 12z OBSERVATION NASA TRMM 3b42_v6 0.25°x0.25° lat/long, 3 hourly EMC REGIONAL MODELS against TRMM 3B42_V6: ITALY DOMAIN 37°- 47.25°N; 6°-19°E PERIOD June –October 2007 All data remapped to 0.25°X0.25° lat/long and accumulated to 18z-18z daily values (models: day1=+30, day2=+54, day3=+78 hrs). Statistics for land points only. MSM 15 km horizontal resolution, 28 vertical levels Relaxed Arakawa Schubert convection scheme OSU LSM (2 layers), heterogeneous b.c. Init. and b.c. data: EMC-GCM T126L28 at 12z REGIONAL MODELS

15. Seasonal Mean (mm/d): summer & fall 2007 JJA SO Both models tend to overestimate rainfall. During summer: 1) overprediction is stronger along the foot-hills belt of the Alps for WRF, in the Po Valley for MSM, 2) overprediction tends to decrease with increasing forecast lead time for MSM. WRF d1 WRF d2 WRF d3 trmm 3b42_V6 MSM d1 MSM d3 MSM d2 WRF d1 WRF d2 WRF d3 trmm 3b42_V6 MSM d1 MSM d3 MSM d2

16. Mean Error (mm/d): summer & fall 2007 WRF d1 WRF d2 WRF d3 MSM d1 MSM d3 MSM d2 JJA WRF d1 WRF d2 WRF d3 MSM d1 MSM d3 MSM d2 SO

17. Continuous Statistics – 10 day running mean RMSE Correlation Coefficient WRF d1 WRF d2 WRF d3 MSM d1 MSM d2 MSM d3 Mean Error Both models overpredict precipitation during all the period investigated, especially WRF during summer, MSM during fall. The models show a similar performance in capturing rainfall amount, but WRF correlates better than MSM with observation, especially during summer

18. Categorical Statistics: light vs. heavy precipitation WRF d1 WRF d2 WRF d3 MSM d1 MSM d2 MSM d3 persistence Both models overestimate rainfall frequency for all thresholds, especially for the highest amounts during summer. Both models outperform persistence. WRF outperforms MSM in detecting rainfall occurrence for all thresholds. JJASO BIAS POD FAR BIAS POD FAR ETS

19. Diurnal Cycle cmorph trmm 3b42 trmm 3b42RT WRF d1 MSM d1 Both models overpredict precipitation, especially during the afternoon (strong convection), and WRF exceeds also with nocturnal summer rains. 3b42RT overestimates precipitation all day long during the summer. cmorph exceeds afternoon rainfall in the summer, while it underestimates precipitation throughout the day, during fall. local time = UTC + 2 hrs JJASO

20. All satellite-derived estimates: reached the best performance in the summertime (when convective rains prevailed), revealed a lower ability in the cold season (when synoptic-scale systems and stratiform precipitation prevailed). cmorph and trmm 3b42 outperformed trmm 3b42RT; 3b42 better captured rain occurrence in all the seasons and rain amount in the warmer months. The models: showed a very different ability in predicting precipitation over Europe, with WRF greatly outperforming RSM (severe problems of overprediction), reached quite a similar performance over Italy (with WRF slightly outperforming MSM), and outperformed persistence. SUMMARY

21. REMARKS & OPEN QUESTIONS HRPP from satellites outperforms only slightly WRF short-term forecasts during the summer. During the bimester of September and October WRF output on the European domain has better scores than cmorph and TRMM 3b42_RT and its results are comparable to TRMM 3b42. What will happen in the next months? Will the model outperform satellite-derived estimates? Could WRF be a good candidate to be combined with satellite-derived estimates to build a more accurate sub-daily precipitation product over Europe? For the smaller domain and the higher resolution model output more work and thought is needed. Thank you!

23. RMSE (mm/d): fall 2006 – spring 2007 trmm 3b42RT trmm 3b42_V6cmorph SON DJF MAM cmorph and trmm 3b42 similar each other and usually better than trmm 3b42RT, except during winter 3b42 shows more errors.

24. RMSE (mm/d): summer & fall 2007 WRF RSM trmm 3b42RTtrmm 3b42_V6cmorph WRF RSM trmm 3b42RTtrmm 3b42_V6cmorph JJA SO Best performance by trmm 3b42, severe problems by RSM.

25. RMSE (mm/d): summer & fall 2007 WRF shows more problems in capturing rainfall amounts next to the alpine regions during summer. WRF d1 WRF d2 WRF d3 MSM d1 MSM d3 MSM d2 JJA WRF d1 WRF d2 WRF d3 MSM d1 MSM d3 MSM d2 SO