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 2 March 2020.
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