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Rainfall estimation by rain gauge‐radar combination: A concurrent multiplicative‐additive approach
Author(s) -
GarcíaPintado Javier,
Barberá Gonzalo G.,
Erena Manuel,
Castillo Victor M.
Publication year - 2009
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2008wr007011
Subject(s) - radar , multiplicative function , rain gauge , interpolation (computer graphics) , mathematics , inverse distance weighting , storm , multivariate interpolation , kernel (algebra) , meteorology , environmental science , statistics , computer science , geography , mathematical analysis , animation , telecommunications , computer graphics (images) , combinatorics , bilinear interpolation
A procedure (concurrent multiplicative‐additive objective analysis scheme [CMA‐OAS]) is proposed for operational rainfall estimation using rain gauges and radar data. On the basis of a concurrent multiplicative‐additive (CMA) decomposition of the spatially nonuniform radar bias, within‐storm variability of rainfall and fractional coverage of rainfall are taken into account. Thus both spatially nonuniform radar bias, given that rainfall is detected, and bias in radar detection of rainfall are handled. The interpolation procedure of CMA‐OAS is built on Barnes' objective analysis scheme (OAS), whose purpose is to estimate a filtered spatial field of the variable of interest through a successive correction of residuals resulting from a Gaussian kernel smoother applied on spatial samples. The CMA‐OAS, first, poses an optimization problem at each gauge‐radar support point to obtain both a local multiplicative‐additive radar bias decomposition and a regionalization parameter. Second, local biases and regionalization parameters are integrated into an OAS to estimate the multisensor rainfall at the ground level. The procedure is suited to relatively sparse rain gauge networks. To show the procedure, six storms are analyzed at hourly steps over 10,663 km 2 . Results generally indicated an improved quality with respect to other methods evaluated: a standard mean‐field bias adjustment, a spatially variable adjustment with multiplicative factors, and ordinary cokriging.