A Bayesian technique for conditioning radar precipitation estimates to rain-gauge measurements

The paper introduces a new technique based upon the use of block-Kriging an d of Kalman filtering to combine, optimally in a Bayesian sense, areal prec ipitation fields estimated from meteorological radar to point measurements of precipitation such as are provided by a network of rain-gauges. The theo retical development is followed by a numerical example, in which an error f ield with a large bias and a noise to signal ratio of 30% is added to a kno wn random field, to demonstrate the potentiality of the proposed algorithm. The results analysed on a sample of 1000 realisations, show that the final estimates are totally unbiased and the noise variance reduced substantiall y. Moreover, a case study on the upper Reno river in Italy demonstrates the improvements in rainfall spatial distribution obtainable by means of the p roposed radar conditioning technique.