Wavelets and Field Forecast Verification

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Abstract
Current field forecast verification measures are inadequate, primarily because they compress the comparison between two complex spatial field processes into one number. Discrete wavelet transforms (DWTs) applied to analysis and contemporaneous forecast fields prove to be an insightful approach to verification problems. DWTs allow both filtering and compact physically interpretable partitioning of fields. These techniques are used to reduce or eliminate noise in the verification process and develop multivariate measures of field forecasting performance that are shown to improve upon existing verification procedures. Abstract Current field forecast verification measures are inadequate, primarily because they compress the comparison between two complex spatial field processes into one number. Discrete wavelet transforms (DWTs) applied to analysis and contemporaneous forecast fields prove to be an insightful approach to verification problems. DWTs allow both filtering and compact physically interpretable partitioning of fields. These techniques are used to reduce or eliminate noise in the verification process and develop multivariate measures of field forecasting performance that are shown to improve upon existing verification procedures.

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