Study on the rainfall dependence structure using radar and rain gauge data

Rainfall processes are characterized by high variability in space and time. Integrating information coming from different sources, such as rain gauges and weather radars, can be useful to better understand the rainfall behaviour in the spacetime domain. In fact, rain gauges can directly provide quantitative rainfall measurements for fine time scales, but the information is only punctual. On the other hand, weather radars provide high space-time resolution data, but the rainfall amounts are indirectly estimated. The paper investigates on properties of rainfall over the North Lazio Region, in Central Italy. The variability of rainfall is examined at time scales from 30 min to 12 hours using a dense rain gauge network and weather radar data. The dependence structure of rainfall (time and space correlation between time series for different time scales) is examined. The analysis are performed first separately for the rain gauge data set (considering the correlation between pairs of rain gauges) and the weather radar data set (considering the correlation between pixels). Then, the correlation between radar and gauge data is examined. The study ends with a discussion of results in order to highlight correlation patterns and decorrelation distances useful in a framework of stochastic model for multisite rainfall simulation.