Weather Radar and Rain-Gauge Data Fusion for Quantitative Precipitation Estimation: Two Case Studies

In recent years, severe weather phenomena have occurred with increasing frequency throughout the Mediterranean area. Because of the extreme intensity of the phenomena and of their small spatio-temporal scales, an early warning of severe rainfall through a timely and accurate estimation is crucial for reducing the hydrological risk and for disaster mitigation. On the other hand, the rain-gauge networks are often not able to detect rainfall due to their limited sampling capability, as occurred in the two case studies presented in this work, characterized by severe weather conditions. For both case studies, we utilized a data-fusion procedure aimed at real-time estimation of cumulative rainfall fields, based on the reflectivity factor Z provided by ground weather radar and rain-gauge estimates of the rainfall intensity R. The use of Z-R relationships determined a priori, as typically done by operational weather services, is not appropriate for obtaining accurate rainfall estimates, as needed, for instance, to forecast flash flood events. Consequently, additional information is needed. The procedure utilized is based on an adaptive data-fusion technique, relying on the dynamic adjustment of the coefficients of the Z-R relationship to the observed phenomenon. Its application to two severe weather case studies demonstrated the capability of the methodology to correctly identify and monitor areas of high potential risk as well as to provide rainfall estimates in such areas.