1. Improving Ensemble QPF in NMC Dr. Dai Kan National Meteorological Center of China (NMC) International Training Course for Weather Forecasters 11/1, 2012, Kunming

2. Outline QPF operations in NMC QPF operations in NMC Improving QPF by ensemble Improving QPF by ensemble Improving PQPF Improving PQPF

3. WFO-- subdivision of NMC (National Meteorological Center) NMC Administrative Office Personnel and Staff Education Division Division of Operational & Reach Management and S. T. development Integrative Office Weather Forecasting Office NWP Operating and Developing Division Typhoon and Marine Met. Division Applied Met. Services Division NMC Agricultural Met. Center Met. Service for Decision-making Office Open Forecast System Laboratory Retirees Office Operations （ 8 ） Management （ 5 ） Severe Weather Prediction Center

4.  QPE  QPF (no PQPF)  Early warning of heavy rain  Precipitation phase in Winter  Total process precipitation forecast QPF’s duties

5. 7-Day 24Hour Precipitation Forecast: Day1-3: Updated Twice a day, at 00,12UTC Day4-7: Updated Once a day, at 00UTC 00UTC 12UTC Threshold: 0.1, 10, 25, 50, 100, 250mm

6. Various observation data Operational determinate model Ensemble model Distinguish weather system QPF verification Ensemble QPF QPF Products Multi-model ensemble QPF Point to point forecast Synoptic situation forecast Grid editing technique QPF revise Blending method QPF Historical data query QPF Gridding Key method QPF technical support and operational process

7. Ensemble system T213-GEPS, 10 days, 15 mem. T213-GEPS, 10 days, 15 mem. WRF-REPS, 60 hours, 15 mem. WRF-REPS, 60 hours, 15 mem. ECMWF, NCEP GEPS ECMWF, NCEP GEPS TIGGE dataset (not real-time, 3 days-delay) TIGGE dataset (not real-time, 3 days-delay)

8. Ensemble analysis and visualization system Ensemble Predication Toolkits V0.3 probabilityspaghettiStamp Box-plot

9. Outline QPF operations in NMC QPF operations in NMC Improving current QPF by ensemble Improving current QPF by ensemble Improving PQPF Improving PQPF

10. Ensemble outputs as a single forecast Mean and spread Max, middle, min %10, %25, %75, %90 quantile Probability-matching ensemble mean (PM)  Compared with deterministic forecast  Advantages and disadvantages of each product  How to improve current operational QPF by ensemble

11. Observations: Longitude: 110~122E Latitude: 28~38N Covering Huaihe catchment 745 observation stations ~0.4 degree space Forecasts: ECMWF global EPS 2007~2012, summer Verification

12. Verifications results Model forecast to stations, 1-day 24h rain rate ~ frequency deterministic forecast, PM approximate to ensemble member Compared with observation curve: — 33mm, under-forecast

13. Verifications results Mean and middle forecast: More under-forecasts for heavy rain No improvement for light or moderate rain

14. Verifications results Max forecast: More over-forecast Close to observation for heavy rain (>150mm) Min forecast: More under-forecast Close to observation for light rain (<10mm)

15. Verifications results 10%25% 75% 90% Close to obs. for different precipitation amount

16. Except PM, no statistic products close to deterministic forecast Each product has advantages and disadvantages  Can we construct a single product which fuse advantages of each product?

17. Fusing product For each grid point, there are 51 member forecast MF. Set fusing value FP = ： (1)If max(MF) >= 100mm, then FP=max(MF); (2)If %90(MF) >= 50mm, then FP= %90(MF) ; (3)If %75(MF) >= 25mm, then FP= %75(MF) ; (4)If middle(MF) >= 10mm, then FP= middle(MF) ; (5)Else FP= %10(MF)

18. Verifications results FP approximate to observations for different precipitation amount

19. Verifications results FP has higher Threat score than deterministic forecast for each precipitation amount Threat score

20. Fusing product （ 1 ） Good for short-range (0~72h) QPF, higher TS than deterministic forecast for different amount rain. （ 2 ） Easily implemented in QPF operations. （ 3 ） Risk of high false alarm ratio, special for medium-range （ 4 ） Threshold decided roughly and subjectively. （ 5 ） In future, use frequency match algorithm to precisely calibrate frequency error.

21. Outline QPF operations in NMC QPF operations in NMC Improving QPF by ensemble Improving QPF by ensemble Improving PQPF Improving PQPF

22. Verifications results 2007~2012, Summer, 1day precipitation – station obs. Under-dispersiveness: U shape of Talagrand histogram

23. Verifications results 2007~2012, Summer, 1day precipitation – station obs. Lack of reliability: Reliability curve not on the diagonal 0.1mm/1Day, Overforecasting (wet bias) 25mm/1Day, Poor resolution (overconfident) 0.1mm/1Day25mm/1Day

24. Verifications results 2007~2012, Summer, 1day precipitation – station obs. Low accuracy for high thresholds: ROC area 0.74 50mm/1Day 50mm/1Day Relative operating characteristic

25. Post-processing To provide reliable forecasts Logistic regression approach Choice of predictors x. Estimation of the b0 and b1 over a training period. Calibrated probabilities p for a threshold T directly addressed.

26. Post-processing Logistic regression approach  Predictors: ensemble mean and spread with 1/3 power transformation  Training period: latest 30 days ; or 2007~2011 5 years summer history forecast (from TIGGE archive )  Forecast period: 2012 summer

27. Post-processing 0.1mm/1day Original 0.1mm/1day Calibration (history forecast) 0.1mm/1day Calibration (30 train days)

28. Post-processing 10mm/1day Original 10mm/1day Calibration (30 train days) 10mm/1day Calibration (history forecast)

29. Post-processing 25mm/1day Original 25mm/1day Calibration (history forecast) 50mm/1day Original 50mm/1day Calibration (history forecast)

30. Logistic Regression PQPF （ 1 ） Calibrate ensemble PQPF effectively （ 2 ） More training samples, more better results （ 3 ） History forecast errors may change with model updating, which influence the calibration. （ 4 ） Reforecast can offer a better way, which we can not gain these dataset.

32. No product close to deterministic forecast Each product has advantages and disadvantages  Can we get a statistic product which close to deterministic forecast or member forecast  Can we construct a product which fuse advantages of each product

33. Probability matching 1. Rank the gridded rainfall from all n QPFs from largest to smallest, the keep every nth value starting with the n/2-th value. 2. Rank the gridded rainfall from the ensemble mean from largest to smallest. 3. Match the two histograms, mapping rain rates from (1) onto locations from (2). (from Beth Ebert )Beth Ebert … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … 1~51 52~102 Rank form largest to smallest Ensemble mean Ensemble member

34. Verifications results PM approximate to ensemble member or deterministic forecast

35. QPF Products Day1: 6-h QPF, updated 3 times a day at 00, 06, 12 UTC

36. Winter: Day1-3 24-h QPF updated twice a day Including the snow, freezing rain, sleet. 24h 48h 72h Precipitation phase forecast:

37. Total process precipitation (for the whole life of a synoptic system)