1. Predicted Rainfall Estimation in the Huaihe River Basin Based on TIGGE Fuyou Tian, Dan Qi, Jingyue Di, and Linna Zhao National Meteorological Center of China Meteorological Administration tianfy@cma.gov.cn 15 September 2009

2. 1.Data and Test Catchment TIGGE 3 centers (CMA, ECMWF and NCEP) total precipitation data Huaihe River Basin, and its sub-catchment 19 Observations in Dapoling-Wangjiaba Reservoir 2. Method Threat Score, Bias Score and Brier Score Percentile 3.Results TS, B and BS Probabilistic forecast of Huaihe River Basin Percentile-based precipitation probabilistic forecast 4.Summary and future works Outline:

3. Meteorological Centers of TIGGE Data Center Country /Domain ModelEnsemble Members Spatial ResolutionForecast Length National Center for Environmental Predictions (NCEP) China Meteorological Administration (CMA) European Center for Medium-Range Weather Forecasts (ECMWF) United States China Europe T126 T213L31 T399L62 T255L62 21 15 51 1°*1° 0.5625°*0.5625° 1°*1° 16 10 1-10 11-15 Total precipitation data from July 1 to August 6, 2008 Accumulated rainfall from 00:00 to 00:00(GMT) of the next day

4. The Test Catchment and Observation Stations Distribution of 19 observation stations in the Dapoling-Wangjiaba sub-region Cumulative rainfall of every observation station from 00:00 to 00:00 of the next day Using the bilinear interpolation method to obtain the grid value 颖河 - 阜阳 涡河 - 蒙 城以上 南四湖区 沂沭水系 蚌埠 - 洪泽湖 王家坝 - 蚌埠 Target basin 大别山库区 淮河下游 Huai-bin Xi-xian Wang-jia-ba

5. The Variation of Daily Areal Rainfall (mm) Extreme event

6. 1.Data and Test Catchment TIGGE 3 centers (CMA, ECMWF and NCEP) total precipitation data Huaihe River Basin, and its sub-catchment 19 Observations in Dapoling-Wangjiaba Reservoir 2. Method Threat Score, Bias Score and Brier Score Percentile 3.Results Probabilistic forecast of Huaihe River Basin Percentile-based precipitation probabilistic forecast 4.Summary and future works

7. Criteria Adopted for Calculation of Rainfall Intensity To calculate the threat score, bias score and brier score, observations and forecasts of daily rainfall are divided into four classes, but very heavy rainfall are not included. The probabilistic and percentile rain not use this criteria, all rainfall intensities are take into consideration. IIIIIIVI <= 0.1 mm No rain 0.1mm~10.0mm Little 10.0mm~25.0mm Moderate 25.0mm~50.0mm Heavy

8. Threat Score (TS), Bias (B) and Brier Score (BS) The Threat Score ( or CSI: Critical Success Index) and Bias (B) are given as (Wilks, D S, 1995) TS = a / (a + b + c) B = (a +b)/ (a + c) where a, b and c represents hits, false alarms, and misses, respectively. TS varies from 0.0 to 1.0, 1.0 indicates the perfect forecast. B=1.0 indicates that the event was forecast the same number of times it was observed. The Brier Score is defined as BS = (f i - o i ) 2 /N in which N is the sample size, the observations o i are all binary, 1.0 if the event occurs and 0 if it doesn’t. The BS ranges from 0 for a perfect forecast to 1.0 for the worst possible forecast.

9. Percentile Firstly set the data in increasing order, a percentile is the value of a variable below which a certain percent of observations or values fall. Values of the target percentiles are estimated using the experience-based equations (Hyndman R J, et al 1996) Q i (p) = (1 - r) X (j) + r X (j+1) in which j = integer (p*n + (1+p)/3) r = p*n + (1+p)/3) – j where Q i (p) presents the returned ith percentile, n is the sample number, X the ordered data.

10. 1.Data and Test Catchment TIGGE 3 centers (CMA, ECMWF and NCEP) total precipitation data Huaihe River Basin, and its sub-catchment 19 Observations in Dapoling-Wangjiaba Reservoir 2. Method Threat Score, Bias Score and Brier Score Percentile 3.Results TS, B and BS Probabilistic forecast of Huaihe River Basin Percentile-based precipitation probabilistic forecast 4.Summary and future works

11. III III IV Results: TS and B of Ensemble Mean over the sub-region

12. Results: TS and B of Control Forecast Member over the sub-catchment III III IV

13. Results: Brier Score over Dapoling-Wangjiaba III III IV

14. A BCD Observation

15. A BCD

16. The 95th percentile precipitation of the 19 observation stations in the Dapoling-Wangjiaba sub-region with a 1 day lead time of three EPSs and their grand ensemble. 2008072300 observation A BC D Observation

17. The 95th percentile precipitation of the 19 observation stations in the Dapoling-Wangjiaba sub-region with a 2 day lead time of three EPS and their grand ensemble. 2008072300 observation A BC D Observation

18. A comparison of probabilistic forecast of daily areal rainfall of the three EPSs and their grand ensemble with a 1 day lead time. The 5th, 25th,50th, 75th, 95th and 99th percentile of daily rainfall are shown, black circles are observations. AB C D

19. Site Scale probability forecast Probabilistic forecast of Daily precipitation of Huaibin Station (115.41 32.45) with a 1 day lead time AB C D

20. Comparison of box and whisker plots for 22 July 2008 at Huaibin station. Black circles are observations (56.2mm).

21. Comparison of box and whisker plots for 16 July 2008 at Huaibin station. Black circles are observations (25.8mm).

22. 1.Data and Test Catchment TIGGE 3 centers (CMA, ECMWF and NCEP) total precipitation data Huaihe River Basin, and its sub-catchment 19 Observations in Dapoling-Wangjiaba Reservoir 2. Method Threat Score, Bias Score and Brier Score Percentile 3.Results Probabilistic forecast of Huaihe River Basin Percentile-based precipitation probabilistic forecast 4.Summary and future works

23. Summary TS and B indicate that every EPS has its advantage, CMA is good at forecast little rain, EC is good at moderate rain BS indicates that grand ensemble take all the probabilities into consideration, and improves the performance Probability of daily rainfall exceeding 25mm/24hrs and 50mm/24hrs show that grand ensemble depicts the spatial distribution well

24. Variation of daily areal rainfall and site scale forecast indicate that grand ensemble has special advantage For forecasters who know little about the performance of every EPS, grand ensemble would be a good choice For hydrological users who pay special attention to key observation stations, grand ensemble based probabilistic forecast would be a good tool Summary

25. WRF 3D-Var (15km×15km) Probabilistic flood floodforecasts Future works VIC model precipitation temperature How to effectively use probabilistic forecast as input? How to show the probabilistic flood forecast?

26. Thanks for your attention! Contact: tianfy@cma.gov.cn

28. A low pressure locates at the SW of Huaihe River Basin, wind shear shows a cyclonic vorticity clearly. Synoptic analyses of 00:00 22 July 2008

29. Synoptic analyses of 12:00 22 July 2008 The low pressure and the cyclonic vorticity move slowly to the south-east, and produced very heavy localized rain.