1 / 21

A Practical Model Blending Technique Based on Bayesian Model Averaging

A Practical Model Blending Technique Based on Bayesian Model Averaging. Bruce Veenhuis bruce.veenhuis@noaa.gov NWS/MDL March 26, 2014 Acknowledgments: John L. Wagner, Michelle Cohen, Mark Oberfield. Motivation.

Télécharger la présentation

A Practical Model Blending Technique Based on Bayesian Model Averaging

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. A Practical Model Blending Technique Based on Bayesian Model Averaging Bruce Veenhuis bruce.veenhuis@noaa.gov NWS/MDL March 26, 2014 Acknowledgments: John L. Wagner, Michelle Cohen, Mark Oberfield

  2. Motivation • Many operational meteorological centers run numerical weather prediction (NWP) models • Deterministic & Ensembles Forecasts • NCEP, Environment Canada, ECMWF • We wish to create a single multi-model consensus • Optimally weight individual models • Create calibrated probability distributions • Mainly concerned with sensible weather elements such as 2-m temperature, 10-m wind speed, etc.

  3. Bayesian Model Averaging (BMA) • A statistical postprocessing technique for ensembles (Rafteryet al. 2005) • BMA dresses each ensemble member with a probabilistic kernel • Combine kernels to create a weighted mean forecast and a reliable probability distribution

  4. Temperature Forecast Model 1 Model 2 Model 3 Predicted Temperature [C]

  5. Temperature Forecast Model 2 Probability Model 1 Model 3 Predicted Temperature [C]

  6. Temperature Forecast Probability Predicted Temperature [C]

  7. Temperature Forecast Probability BMA Mean Predicted Temperature [C]

  8. Temperature Forecast Probability Predicted Temperature [C]

  9. Temperature Forecast Probability Predicted Temperature [C]

  10. Updatable BMA (UBMA) • MDL’s implementation of BMA • First apply decaying average bias correction to each model • Continuously updates the weights and standard deviations with a decaying average algorithm • Training is based on recent performance • Simple to implement and computationally cheap

  11. UBMA Basics Previous Model Forecasts Latest Observation Temperature Forecast Estimate Weights & Stddevs Observation Model 2 Probability Model 1 Model 3 Predicted Temperature [C]

  12. UBMA Basics Previous Weights & Stddevs Previous Model Forecasts Latest Observation x 0.95 Estimate Weights & Stddevs Decaying Average Update x 0.05

  13. UBMA Basics Previous Weights & Stddevs Previous Model Forecasts Latest Observation x 0.95 Estimate Weights & Stddevs Decaying Average Update x 0.05 New Weights & Stddevs

  14. UBMA Basics Previous Weights & Stddevs Previous Model Forecasts Latest Observation x 0.95 Estimate Weights & Stddevs Decaying Average Update x 0.05 New Weights & Stddevs Make UBMA Forecast Latest Model Forecasts

  15. Example Application 1:Consensus MOS with UBMA • MDL creates a variety of MOS guidance • GFS • NAM • ECMWF • EKDMOS Mean (GEFS and CMCE) • 1 November 2011 – 31 March 2012 • 2-m Temperature, 335 Stations • Accuracy and Reliability

  16. UBMA

  17. Cumulative Reliability Diagrams 2-m Temperature 48-hr 2-m Temperature 102-hr Observed Cumulative Freq. Observed Cumulative Freq. Predicted Cumulative Prob. Predicted Cumulative Prob.

  18. Example Application 2:UBMA Applied to SREF • Calibrated 850 hPa temperature forecasts from the Short Range Ensemble Forecast (SREF) • To support precipitation type forecasting • NDAS analysis: proxy for truth • SREF and NDAS interpolated to stations • Experimental Products Available: http://www.mdl.nws.noaa.gov/~BMA-SREF/BMAindex.php

  19. Probability Integral Transform (PIT) Histograms 48-hr Projection UBMA Raw Members 2.53

  20. Conclusions • UBMA – MDL’s implementation of BMA • Estimate weights and standard deviations with a decaying average algorithm • Pros: • Computationally cheap • Simple to implement • Improves accuracy and reliability • Cons: • Can only increase ensemble spread • May be problematic with an overdispersed ensemble • UBMA only tested for Gaussian elements

More Related