A Morphing-Based Technique for the Verification of Precipitation Forecasts

Abstract

Abstract This paper describes a morphing-based approach for the verification of precipitation forecasts. This approach employs a pyramid matching algorithm to morph the precipitation features in a forecast into features that match the related precipitation features in the verifying analysis (observations) as closely as possible. The algorithm computes an optical flow (vector field) that maps the original forecast features into the morphed forecast features. The optical flow also provides quantitative information about the error in the location of the forecast features. This information, combined with information about the error in the prediction of the total precipitation over the verification domain, is used to quantify the structure error in the precipitation forecast. The proposed approach has three novel aspects compared to the published morphing-based verification strategies. First, it imposes a constraint on the pyramid matching algorithm to prevent overconvergence toward strong precipitation features during morphing. Second, it introduces an objective criterion for the selection of the subsampling parameter to avoid splitting or distorting features due to an arbitrary maximum displacement limit. Third, the proposed definitions of the location and structure errors are new. The behavior of the proposed multivariate verification metrics is investigated by applications to both idealized and numerical forecast examples.