A Bayesian technique for conditioning radar precipitation estimates to rain-gauge measurements
By Ezio Todini. 2001
176 citations recorded by the source corpus.
Abstract. 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
Subfields: Atmospheric Science, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/c9e587a8a4569cc9f4095ec36e68c05e6035b54f
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Atmospheric Science
In Global and Planetary Change
Citations in the corpus
- Geostatistical radar-raingauge combination with nonparametric correlograms: methodological considerations and application in Switzerland (2010, unknown): “…(ii) the disaggregation of gauge fields by radar information (e.g., DeGaetano and Wilks, 2009; W¨uest et al., 2009), and (iii) geostatistical combination methods (Seo et al., 1990; Seo, 1998; Todini, 2001; Sinclair and Pegram, 2005; Haber-landt, 2007; Erdin, 2009; Velasco-Forero et al., 2009).”
- Probabilistic Quantitative Precipitation Estimation in Complex Terrain (2005, background): “One approach is to explicitly account for the uncertainty of parameter estimates that are used to define the kriging covariance matrices (Hughes and Letten-maier 1981; Kitanidis 1986; Todini 2001b; Todini et al. 2001).”