Post-processing ensemble forecasts is generally a necessary requirement to provide meaningful probabilistic guidance to users. One approach that has been used for a variety of applications is quantile regression (QR). RAL scientists are applying a novel statistical correction approach by combining QR with other post-processing approaches (e.g. analog, logistic regression) to calibrate at the specific probability intervals required by the user. Some of the benefits of this approach are that no assumptions are required on the form of the forecast probability distribution function to attain optimality; the resultant forecast skill is no worse than a forecast of either climatology or persistence; and the generated ensembles have dispersive properties directly related to the uncertainty in the forecast that one would expect.
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Post-Processing