A Study on the Scale Dependence of the Predictability of Precipitation Patterns

Abstract

methodology is proposed to investigate the scale dependence of the predictability of precipitation patterns at the mesoscale. By applying it to two or more precipitation fields, either modeled or observed, a decorrelation scale can be defined such that all scales smaller than are fully decorrelated. For precipitation forecasts from a radar data?assimilating storm-scale ensemble forecasting (SSEF) system, is found to increase with lead time, reaching 300 km after 30 h. That is, for , the ensemble members are fully decorrelated. Hence, there is no predictability of the model state for these scales. For , the ensemble members are correlated, indicating some predictability by the ensemble. When applied to characterize the ability to predict precipitation as compared to radar observations by numerical weather prediction (NWP) as well as by Lagrangian persistence and Eulerian persistence, increases with lead time for most forecasting methods, while it is constant (300 km) for non?radar data?assimilating NWP.Comparing the different forecasting models, it is found that they are similar in the 0?6-h range and that none of them exhibit any predictive ability at meso-? and meso-? scales after the first 2 h. On the other hand, the radar data?assimilating ensemble exhibits predictability of the model state at these scales, thus causing a systematic difference between corresponding to the ensemble and corresponding to model and radar. This suggests that either the ensemble does not have sufficient spread at these scales or that the forecasts suffer from biases.