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Simple- and Modified- Poor Man's QPF Ensembles and a Neural Network Approach

Ying Lin and Vladimir Krasnopolsky NOAA/NWS/NCEP/EMC AMS 24 th Conf on W&F/20 th Conf on NWP 25 Jan 2011. Simple- and Modified- Poor Man's QPF Ensembles and a Neural Network Approach. A simple Multi-model QPF Ensemble.

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Simple- and Modified- Poor Man's QPF Ensembles and a Neural Network Approach

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  1. Ying Lin and Vladimir Krasnopolsky NOAA/NWS/NCEP/EMC AMS 24th Conf on W&F/20th Conf on NWP 25 Jan 2011 Simple- and Modified- Poor Man's QPF Ensemblesand a Neural Network Approach

  2. A simple Multi-model QPF Ensemble “MEDLEY”: simple arithmetic averaging of eight operational forecasting models: NCEP's NAM and GFS CMC (Canadian Meteorological Centre) regional and global models DWD (Deutscher Wetterdienst) ECMWF (European Centre for Medium-Range Weather Forecasts) JMA (Japan Meteorological Agency) UKMO (UK Met Office)

  3. 00-24h Forecast ending 2010072412 Verifying analysis MEDLEY NAM GFS

  4. MEDLEYand the 8 “member” models, 24+48h fcstsJan – Dec 2010 ETS Bias

  5. Comparing MEDLEY to Component Models Advantages: better placement of precipitation areas, overall higher ETS Disadvantages (from simple averaging): High bias in low thresholds – large area of low precip Low bias in high thresholds – highs smoothed out

  6. How to refine “MEDLEY”? Excluding worst performers from the ensemble: MEDLEY2: as in MEDLEY, but exclude the two models with the worst performance (average ETS for the thresholds of 0.1”, 0.25”, 0.5”, 0.75” and 1”/day) in the preceding 30 days

  7. MEDLEY, MEDLEY2 and the 8 “member” models, 24+48h fcsts, Jan – Dec 2007 ETS Bias

  8. Probability Matched “MEDLEY” Let the rain rate distribution in the ensemble mean match that of the rain rate distribution of the pooled ensemble members (Ebert, 2001).

  9. MEDLEY and MEDLEY3 (probability-matched) and the 8 “member” models, 24+48h fcsts, Jan – Dec 2010 ETS Bias

  10. MEDLEY, MEDLEY3 (PM), HPC, NAM and GFS, 24+48h fcsts, 12 Jul – 31Dec 2010 Bias ETS

  11. 00-24h Forecast ending 2010072412 Verifying analysis HPC MEDLEY MEDLEY3(PM)

  12. A neural network approach Develop a non-linear multi-model ensemble. Use past model QPF and verifying analysis data (precip amounts, Julian dates, lat/lon of each data point) to “train” the NN. Additional info: Krasnopolsky, “ Application of the neural network technique to develop a nonlinear multi-model ensemble for precipitation over ConUS”. Paper 1.1, 9th Conf. on Artificial Intelligence and its Application to the Environmental Sciences.

  13. Sample NN forecast: 00-24h fcst ending 2010102412, 1 of 2 Verifying analysis GFS NAM HPC

  14. Sample NN forecast: 00-24h fcst ending 2010102412, 2 of 2 Verifying analysis MEDLEY MEDLEY3 (PM) NN2

  15. Conclusions Simple ensemble mean produces a product that is too “smoothed out” Withholding two poorly-performing members from the ensemble improves performance slightly Probability-matching improves ensemble greatly Neural network approach promising – more details are preserved

  16. Future Plans “MEDLEY” on a higher resolution grid to benefit from higher resolution members Bias adjustment to individual members' precipitation forecasts (as in SREF – Jun Du) Further refine neural network approach

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