Journal
HYDROLOGY AND EARTH SYSTEM SCIENCES
Volume 5, Issue 2, Pages 187-199Publisher
COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/hess-5-187-2001
Keywords
rainfall; meteorological radar; Bayesian technique; block-Kriging; Kalman filtering
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The paper introduces a new technique based upon the use of block-Kriging and of Kalman filtering to combine, optimally in a Bayesian sense, areal precipitation fields estimated from meteorological radar to point measurements of precipitation such as are provided by a network of rain-gauges. The theoretical development is followed by a numerical example, in which an error field with a large bias and a noise to signal ratio of 30% is added to a known 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 substantially. Moreover, a case study on the upper Reno river in Italy demonstrates the improvements in rainfall spatial distribution obtainable by means of the proposed radar conditioning technique.
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