SAL—A Novel Quality Measure for the Verification of Quantitative Precipitation Forecasts

Abstract A novel object-based quality measure, which contains three distinct components that consider aspects of the structure (S), amplitude (A), and location (L) of the precipitation field in a prespecified domain (e.g., a river catchment) is introduced for the verification of quantitative precipitation forecasts (QPF). This quality measure is referred to as SAL. The amplitude component A measures the relative deviation of the domain-averaged QPF from observations. Positive values of A indicate an overestimation of total precipitation; negative values indicate an underestimation. For the components S and L, coherent precipitation objects are separately identified in the forecast and observations; however, no matching is performed of the objects in the two datasets. The location component L combines information about the displacement of the predicted (compared to the observed) precipitation field’s center of mass and about the error in the weighted-average distance of the precipitation objects from the total field’s center of mass. The structure component S is constructed in such a way that positive values occur if precipitation objects are too large and/or too flat, and negative values if the objects are too small and/or too peaked. Perfect QPFs are characterized by zero values for all components of SAL. Examples with both synthetic precipitation fields and real data are shown to illustrate the concept and characteristics of SAL. SAL is applied to 4 yr of daily accumulated QPFs from a global and finer-scale regional model for a German river catchment, and the SAL diagram is introduced as a compact means of visualizing the results. SAL reveals meaningful information about the systematic differences in the performance of the two models. While the median of the S component is close to zero for the regional model, it is strongly positive for the coarser-scale global model. Consideration is given to the strengths and limitations of the novel quality measure and to possible future applications, in particular, for the verification of QPFs from convection-resolving weather prediction models on short time scales.