Recalibration of cumulative rainfall estimates by weather radar over a large area

The real-time measurement of rainfall is a primary information source for many purposes, such as weather forecasting, flood risk assessment, and landslide prediction and prevention. In this perspective, remote sensing techniques to monitor rainfall fields by means of radar measurements are very useful. In this work, a technique is proposed for the estimation of cumulative rainfall fields averaged over a large area, applied on the Tuscany region using the Italian weather radar network. In order to assess the accuracy of radar-based rainfall estimates, they are compared with coincident spatial rain gauge measurements. Observations are compared with average rainfall over areas as large as a few tens of kilometers. An ordinary block kriging method is applied for rain gauge data spatialization. The comparison between the two types of estimates is used for recalibrating the radar measurements. As a main result, this paper proposes a recalibrated relationship for retrieving precipitation from radar data. The accuracy of the estimate increases when considering larger areas: an area of 900 km2 has a standard deviation of less than few millimeters. This is of interest in particular for extending recalibrated radar relationships over areas where rain gauges are not available. Many applications could benefit from it, from nowcasting for civil protection activities, to hydrogeological risk mitigation or agriculture.