SAD: Verifying the scale, anisotropy and direction of precipitation forecasts

Abstract

One important attribute of meteorological forecasts is their representation of spatial structures. While several existing verification methods explicitly measure a structure error, they mostly produce a single value with no simple interpretation. Extending a recently developed wavelet‐based verification method, this study separately evaluates the predicted spatial scale, orientation and degree of anisotropy. The scale component has been rigorously tested in previous work and is known to assess the quality of a forecast similar to other, established methods. However, directional aspects of spatial structure are less frequently considered in the verification literature. Since important weather phenomena related to fronts, coastlines and orography have distinctly anisotropic signatures, their representation in meteorological models is clearly of interest. The ability of the new wavelet approach to accurately evaluate directional properties is demonstrated using idealized and realistic test cases from the MesoVICT project. A comparison of precipitation forecasts from several forecasting systems reveals that errors in scale and direction can occur independently and should be treated as separate aspects of forecast quality. In a final step, we use the inverse wavelet transform to define a simple post‐processing algorithm that corrects the structural errors. The procedure improves visual similarity with the observations, as well as the objective scores.

Forecasts of precipitation fields are difficult to evaluate due to their complex, intermittent spatial structure. The SAD forecast verification method uses wavelets to compare the scale (colours in the top row), anisotropy (bottom, arrow length) and preferred direction (bottom, arrow angles) of simulated and observed fields. The new approach is successfully tested using data from the MesoVICT community project.