A Comparison between Raw Ensemble Output, (Modified) Bayesian Model Averaging, and Extended Logistic Regression Using ECMWF Ensemble Precipitation Reforecasts

Abstract Using a 20-yr ECMWF ensemble reforecast dataset of total precipitation and a 20-yr dataset of a dense precipitation observation network in the Netherlands, a comparison is made between the raw ensemble output, Bayesian model averaging (BMA), and extended logistic regression (LR). A previous study indicated that BMA and conventional LR are successful in calibrating multimodel ensemble forecasts of precipitation for a single forecast projection. However, a more elaborate comparison between these methods has not yet been made. This study compares the raw ensemble output, BMA, and extended LR for single-model ensemble reforecasts of precipitation; namely, from the ECMWF ensemble prediction system (EPS). The raw EPS output turns out to be generally well calibrated up to 6 forecast days, if compared to the area-mean 24-h precipitation sum. Surprisingly, BMA is less skillful than the raw EPS output from forecast day 3 onward. This is due to the bias correction in BMA, which applies model output statisti...

[1]  H. Glahn,et al.  The Use of Model Output Statistics (MOS) in Objective Weather Forecasting , 1972 .

[2]  Klimaatatlas van Nederland , 1972 .

[3]  Leonard A. Smith,et al.  Combining dynamical and statistical ensembles , 2003 .

[4]  Thomas M. Hamill,et al.  Ensemble Reforecasting: Improving Medium-Range Forecast Skill Using Retrospective Forecasts , 2004 .

[5]  A. Raftery,et al.  Using Bayesian Model Averaging to Calibrate Forecast Ensembles , 2005 .

[6]  Anton H. Westveld,et al.  Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation , 2005 .

[7]  A. Sterl,et al.  The ERA‐40 re‐analysis , 2005 .

[8]  Philip H. Ramsey.  Statistical Methods in the Atmospheric Sciences , 2005 .

[9]  C. Bishop,et al.  Improvement of ensemble reliability with a new dressing kernel , 2005 .

[10]  Maurice Schmeits,et al.  Probabilistic Forecasting of (Severe) Thunderstorms in the Netherlands Using Model Output Statistics , 2005 .

[11]  E. Lorenz.  Predictability of Weather and Climate: Predictability – a problem partly solved , 2006 .

[12]  Daniel S. Wilks,et al.  Comparison of ensemble‐MOS methods in the Lorenz '96 setting , 2006 .

[13]  Thomas M. Hamill,et al.  Probabilistic Quantitative Precipitation Forecasts Based on Reforecast Analogs: Theory and Application , 2006 .

[14]  Thomas M. Hamill.  Comments on “Calibrated Surface Temperature Forecasts from the Canadian Ensemble Prediction System Using Bayesian Model Averaging” , 2007 .

[15]  Thomas M. Hamill,et al.  Comparison of Ensemble-MOS Methods Using GFS Reforecasts , 2007 .

[16]  W. Briggs.  Statistical Methods in the Atmospheric Sciences , 2007 .

[17]  J. M. Sloughter,et al.  Probabilistic Quantitative Precipitation Forecasting Using Bayesian Model Averaging , 2007 .

[18]  M. Kenward,et al.  An Introduction to the Bootstrap , 2007 .

[19]  A. Raftery,et al.  Calibrated Surface Temperature Forecasts from the Canadian Ensemble Prediction System Using Bayesian Model Averaging , 2007 .

[20]  Renate Hagedorn,et al.  Probabilistic Forecast Calibration Using ECMWF and GFS Ensemble Reforecasts. Part II: Precipitation , 2008 .

[21]  Maurice Schmeits,et al.  Probabilistic Forecasts of (Severe) Thunderstorms for the Purpose of Issuing a Weather Alarm in the Netherlands , 2008 .

[22]  C. Bishop,et al.  Bayesian Model Averaging’s Problematic Treatment of Extreme Weather and a Paradigm Shift That Fixes It , 2008 .

[23]  Daniel S. Wilks,et al.  Extending logistic regression to provide full‐probability‐distribution MOS forecasts , 2009 .

[25]  A. Raftery,et al.  Calibrating Multimodel Forecast Ensembles with Exchangeable and Missing Members Using Bayesian Model Averaging , 2010 .

[26]  Christopher F. Parmeter,et al.  Bayesian Model Averaging in R , 2011 .