Postprocessing

The use of statistical techniques to transform numerical weather prediction (NWP) output (possibly ensemble output) into a prediction of a meteorological variable or variables, often with the aim of improving meteorological guidance and decision-making. The transformation may incorporate data from sources outside the NWP (e.g., observations or climatology) and can take many forms. For example, it may include a first-moment correction (i.e., bias correction) or a correction of higher moments (e.g., ensemble resampling or dispersion correction), or it may produce the forecast of a derived variable not explicitly part of the NWP output (e.g., fog or maximum wind over some period of time). Postprocessing can be a multistep process and involve a variety of statistical techniques (e.g., regression analysis or discriminant analysis). Most postprocessing techniques fall into the categories of model output statistics (MOS) and perfect prog (PP).

Term edited 14 August 2018.

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