Statistical Post-processing of Operational & CDC Hindcast Ensembles
Bo Cui, EMC, Camp Springs, MD; and Z. Toth, Y. Zhu, D. Hou, and S. Beauregard
Statistical post-processing algorithms are being developed at the National Centers for Environmental Prediction (NCEP) of the US National Weather Service (NWS) for eliminating the bias from the NWS and the Meteorological Service of Canada (MSC) ensemble forecasts before they are merged to form a joint ensemble within the North American Ensemble Forecast System (NAEFS). Preliminary results show that an adaptive, regime dependent bias correction method works well for the first few days. The calibrated NCEP operational ensemble after removing the time mean forecast errors for the most recent period have improved probabilistic performance for all measures till day 5. The reforecast ensembles from the Climate Prediction Center (CDC) with and without climate mean bias correction are also examined. A comparison between the operational and CDC bias-corrected ensemble forecasts shows that climate mean bias correction can add value, especially for week 2 probability forecasts. The methods developed for bias correction at the NWS and the MSC will be compared, and the best performing methods will be selected for use at both centers within the NAEFS system. The new method will also be tested in the context of the Bayesian Model Averaging algorithm in development at MSC to try to improve upon the associated simple linear bias correction scheme. New bias correction methods developed under the Observing-system Research and predictability experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) project will also be considered for use in the NAEFS system. .
Session 12B, Ensembles II
Thursday, 4 August 2005, 10:30 AM-12:00 PM, Ambassador Ballroom
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