Tuesday, 8 January 2013: 8:30 AM
Room 18A (Austin Convention Center)
Forecast systems such as the Dynamic Integrated ForeCast system (DICast) produce statistically enhanced forecasts that could be used as the basis for passed to a probabilistic forecast. Probabilistic forecast techniques, such as Bayesian Model Averaging (BMA), typically use bias-corrected raw model data from each ensemble member. However, other calibration techniques, such as statistical post-processing of raw model data, are typicallyoften better than simple bias correction. This talk will present results of a study that compares whether statistically enhanced inputs to a probabilistic forecast process actually result in improved probabilistic forecasts.
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