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<div class="definition"><div class="short_definition">(Abbreviated MOS.) For a [[numerical weather prediction]] model, [[statistical]]  relations between model-forecast [[variables]] and observed weather variables, used for either [[correction]]  of model-forecast variables or [[prediction]] of variables not explicitly forecast by the [[model]].</div><br/> <div class="paragraph">They have often taken the form of multilinear [[regression]] equations derived by screening  potential model-forecast variables as predictors. The method produces forecasts of weather variables  that to some extent account for the [[random]] and [[systematic errors]] in the [[numerical weather  prediction]] model. <br/>''Compare'' [[perfect prognosis method]].</div><br/> </div>
<div class="definition"><div class="short_definition">(Abbreviated MOS.) For a [[numerical weather prediction]] model, [[statistical]]  relations between model-forecast [[variables]] and observed weather variables, used for either [[correction]]  of model-forecast variables or [[prediction]] of variables not explicitly forecast by the [[model]].</div><br/> <div class="paragraph">They have often taken the form of multilinear [[regression]] equations derived by screening  potential model-forecast variables as predictors. The method produces forecasts of weather variables  that to some extent account for the [[random error|random]] and [[systematic errors]] in the [[numerical forecasting|numerical weather  prediction]] model. <br/>''Compare'' [[perfect prognosis method]].</div><br/> </div>
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Latest revision as of 16:27, 25 April 2012



model output statistics

(Abbreviated MOS.) For a numerical weather prediction model, statistical relations between model-forecast variables and observed weather variables, used for either correction of model-forecast variables or prediction of variables not explicitly forecast by the model.

They have often taken the form of multilinear regression equations derived by screening potential model-forecast variables as predictors. The method produces forecasts of weather variables that to some extent account for the random and systematic errors in the numerical weather prediction model.
Compare perfect prognosis method.


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