Combining Raingages and Radar Precipitation Measurements Using a Bayesian Approach

Abstract

This paper examines a new technique, based upon the combination of block kriging and Kalman filter in order to optimally combine, in a Bayesian sense, spatial precipitation fields estimated from meteorological radar with the same fields estimated from point measurements of precipitation, such as the ones provided by a network of rain gauges. The Bayesian combination technique is tested by means of a numerical example, in order to demonstrate the potentiality of the proposed algorithm and to compare it with the methods developed in the past. The new method is shown to be superior, both in terms of bias and variance reduction, to the available ones, from Brandes’ method (or similar) based on Barnes’ objective analysis scheme, to the co-kriging approach.