Identification of Systematic Errors in a Numerical Weather Forecast

Abstract

Many numerical model verification schemes are handicapped by their inability to separate non-systematic errors and systematic errors. In this study, for a specific synoptic event, a statistical method is described to determine a minimum number of cases which can be averaged to represent numerical forecast errors which are truly systematic and not smoothed fields of rapidly varying non-systematic errors. Error patterns derived from forecasts and observations stored at Fleet Numerical Oceanography Center are used to compare a systematic error pattern, defined by the total number of available cases with subset error patterns to determine the minimum number of cases needed to filter out the unwanted non-systematic error components. The analysis indicates that a minimum of 8 cases must be averaged to adequately identify systematic errors in a 24 h forecast of a Shanghai Low. A minimum of 5 cases are needed for a 72 h forecast of the same event. Error patterns are identified by contours of the Student's t statistic calculated at each grid point. This contour pattern objectively determines the significance of the forecast errors and is shown to be a very useful method of portraying, systematic forecast errors.