Many atmospheric phenomena like fronts, convection and turbulence leave a distinct imprint on the spatial structure of meteorological fields such as precipitation, wind and temperature. Whether or not a forecast model is able to realistically simulate the resulting spatial correlation patterns is therefore a relevant question for model developers, forecasters and end users alike. Highly resolved numerical models have the potential to achieve this goal, but their realism is often difficult to assess objectively due to the sheer amount of data and wide variety of possible error contributions.
While some existing verification methods measure an overall “structure” error, most of these approaches are limited to precipitation fields and fail to produce specific, interpretable judgements. Here, we introduce a new structural verification technique based on the dual-tree complex wavelet transformation: The SAD-scores explicitly quantify how well the observed spatial Scales, degrees of Anisotropy and preferred Directions are represented by the simulation. Directional aspects in particular have previously often been neglected, but can be important in assessing the realism of predicted fronts, convergence lines and organized convection.
Unlike many established techniques, SAD is applicable not only to precipitation but to any meteorological field of interest. General verdicts like “the structure was predicted poorly” can be resolved into specific statements like “the modelled convection was too small in scale” or “the simulated front was too linear and rotated by an angle of X degrees”. The localized nature of the wavelets furthermore allows us to conveniently display the structural properties on a map. Lastly, making use of the inverse wavelet transform, we show how the detected structural errors can potentially be corrected, thereby leading the way towards future post-processing applications.
How to cite: Buschow, S. and Friederichs, P.: Making forecasters SAD: Verification of Scale, Anisotropy and Direction using wavelets., EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-325, https://doi.org/10.5194/ems2021-325, 2021.