Radar calibration of a cluster spatial-temporal model of rainfall: a case study

In small and medium size catchments the variability of rainfall fields leads to biases in rain estimation: for this reason stochastic spatial-temporal models of precipitation play a very important role for the simulation of flood events. The calibration of the GDSTM model, a cluster stochastic generation model in continuous space as well as time, is presented. In this model storms arrive in a Poisson process in time with cells occurring in each storm that cluster in space and time. The model is calibrated using data collected by the weather radar Polar 55C in Rome, over an area of 100x100 km2, with the radar located at the center. The parameters are estimated with the Hansen method, using data with a resolution of 2x2 km2 space-time.