| 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.
Keywords: Rainfall, meteorological radar, Bayesian technique, block-Kriging,
Kalman filtering |