Estimate of errors in lagged time average numerical weather prediction

Abstract

An idealized model for error growth is used to discuss numerical weather prediction model forecasts of time averages ranging from 0 to 30 days in duration at lags of 0 to 30 days. The idealized model allows a simple assessment of how the initial error, error growth rate and serial correlation, influence a forecast model's prediction of a time average. The simplified nature of the idealized model also allows an easy demonstration of how various filters applied to the raw numerical predictions can help to improve forecast skill.