Impact of Different Spatial Domain Decomposition Approaches on a Spectral-Based Nowcasting Model Implemented at Italian National Scale

The implementation strategy of a nowcasting methodology can be crucial to pursue skillful results in an operational context to obtain reliable short forecasts with as much as possible reduced errors. In this work, a spectral nowcasting algorithm was considered to carry out rainfall prediction at the Italian national scale, instead of the traditional “single-piece area” approach; strategies were tested to dynamically split the precipitation zone into smaller sub-regions by identifying connected components within the precipitation area. These strategies rely on image-processing techniques, and they were tested over a long period of time which includes several events with a variety of rainfall typologies (stratiform, thunderstorms, persistent rainfall). Traditional standard skill scores widely used in hydro-meteorology are exploited to quantify the improvements. The strategy that leads to the best performance is the one that results in smaller spatial calculation domains; this demonstrates the importance of correctly modeling and interpreting the different types of rain structures that may be present simultaneously in the rain field across a large domain.