The use of statistical
techniques to transform numerical weather prediction
(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
) 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
) and perfect prog
Term edited 2 March 2020.
Copyright 2022 American Meteorological Society (AMS). For permission to reuse any portion of this work, please contact [email protected]. Any use of material in this work that is determined to be “fair use” under Section 107 of the U.S. Copyright Act (17 U.S. Code § 107) or that satisfies the conditions specified in Section 108 of the U.S.Copyright Act (17 USC § 108) does not require AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a website or in a searchable database, or other uses of this material, except as exempted by the above statement, require written permission or a license from AMS. Additional details are provided in the AMS Copyright Policy statement.