1 / 37

Improving Ensemble QPF in NMC

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

bary
Download Presentation

Improving Ensemble QPF in NMC

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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 • Improving QPF by ensemble • Improving PQPF

  3. WFO-- subdivision of NMC (National Meteorological Center) Administrative Office Personnel and Staff Education Division NMC Management （5） Division of Operational & Reach Management and S. T. development Integrative Office Retirees Office Weather Forecasting Office NWP Operating and Developing Division Typhoon and Marine Met. Division Applied Met. Services Division Operations （8） NMC Agricultural Met. Center Met. Service for Decision-making Office Open Forecast System Laboratory 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 12UTC 00UTC Threshold: 0.1, 10, 25, 50, 100, 250mm

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

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

  8. Ensemble analysis and visualization system Ensemble Predication Toolkits V0.3 probability spaghetti Stamp Box-plot

  9. Outline • QPF operations in NMC • Improving current QPF by ensemble • 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. Verification • Observations: • Longitude: 110~122ELatitude: 28~38NCovering Huaihe catchment • 745 observation stations~0.4 degree space • Forecasts: • ECMWF global EPS • 2007~2012, summer

  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, over-forecast — >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% Close to obs. for different precipitation amount 75% 90%

  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 = ： • If max(MF) >= 100mm, then FP=max(MF); • If %90(MF) >= 50mm, then FP= %90(MF) ; • If %75(MF) >= 25mm, then FP= %75(MF) ; • If middle(MF) >= 10mm, then FP= middle(MF) ; • Else FP= %10(MF)

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

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

  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 decidedroughly and subjectively. （5）In future, use frequency match algorithm to precisely calibrate frequency error.

  21. Outline • QPF operations in NMC • Improving QPF by ensemble • 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/1Day 25mm/1Day

  24. Verifications results 2007~2012, Summer, 1day precipitation – station obs. • Low accuracy for high thresholds: • ROC area 0.74 < 0.8 for thresholds > 50mm/1Day Relative operating characteristic 50mm/1Day

  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 (history forecast) 10mm/1day Calibration (30 train days)

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

  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.

  31. Thanks!

  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 ) Ensemble mean … … … … … 1~51 … … … … … … … … … … … … … … … … Ensemble member … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … 52~102 … … … Rank form largest to smallest

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

  35. QPFProducts 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 24h 72h 48h Precipitation phase forecast: • Including the snow, freezing rain, sleet.

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

More Related