T1 - Development of a predictability index for severe weather events over Europe
Other researcher: Federico Grazzini (PhD)
Accurate and timely prediction of high impact weather, and in particular intense precipitation events, is essential to limit losses of life and property. Although predictability is fundamentally limited by the chaotic nature of the atmosphere, recent research in atmospheric dynamics has provided new insights into the processes that limit the accuracy of forecasts, which may be of significant use in operational practice. The aim of this project is to evaluate some recently developed measures of how different atmospheric processes influence predictability, and to combine them to formulate a new forecasting methodology to provide guidance in the interpretation of forecasts of heavy precipitation.
Rossby wave packet diagnostic tools, developed in the framework of W2W, will be applied to ECMWF analysis and forecast products in order to obtain a robust quantification of the presence and impact of upper level precursors to the heavy precipitation events. The statistics of past basin-area heavy precipitation events will be investigated using the newly released high resolution precipitation dataset ArCIS (Climatological Archive for Central Northern Italy). A convective adjustment time scale will be computed to provide a measure of how closely the precipitation is coupled to the large-scale weather pattern. Combining these statistics, we aim to define a predictability index that reflects the degree to which predictability is imposed by the large scale, but diminished by the unpredictability of the small scale. This index, designed for operational usage, will provide forecasters and other end users with a measure of which scales of motion can be accurately predicted for a particular weather forecast.