Ensemble forecast bias correction
Angeline Greene Pendergrass, National Weather Center REU, University of Oklahoma, Coral Gables, FL; and K. L. Elmore
This study investigates two bias correction methods, lagged average and lagged linear regression, for individual members of ensemble forecasts. Both methods use the forecast bias from previous forecasts to predict the bias of the current forecast at every station. Also considered is the training period length that results in the smallest forecast error.
Ensemble forecast and verification data span 23 July through 15 September 2003. The data are organized into a mini-ensemble composed of 5 models and 30 days. This mini-ensemble is corrected using each method of correction for training period lengths between 3 and 12 days. The resulting bias, mean absolute error, RMS error, and inter-quartile range of the corrected forecasts are then compared.
Forecasts corrected with the lagged linear regression method are less biased but have more variance than those corrected with the lagged average method.
Poster Session 1, Student Conference Poster Session
Sunday, 9 January 2005, 5:30 PM-5:30 PM
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