Extended Abstract (768K)P4.3
Toward an error covariance matrix of radar rainfall estimates
Marc Berenguer, McGill University, Montreal, QC, Canada; and I. Zawadzki
There are a number of applications that need a good characterization of the errors affecting radar rainfall estimates, not only in terms of their magnitude, but also in terms of their correlation both in time and space. In particular, most of NWP schemes assimilating radar measurements use diagonal, homogenous error covariance matrices as a simplified approach. Recently, a number of studies have proposed to characterize the residuals between radar estimates and “reference” rainfall measurements (mainly, from rain gages). As an alternative, the structure of the different error sources affecting radar rainfall estimates and the cross-correlation between them is studied here. In particular, we focus on two of the most important sources of uncertainty affecting radar estimates at non-attenuated wavelengths: (i) the error associated with range (including the VPR problem and the effect of beam broadening) and (ii) the uncertainty associated to the Z-R transformation. The structure of error with range, magnitude and correlation, has been studied by simulating radar measurements at different ranges from real reflectivity profiles measured close to the radar. The covariance between range dependent errors and those due to drop size distribution variability has been analyzed by comparing the simulated reflectivity profiles and the measurements of a POSS disdrometer, used to quantify the uncertainty in the Z-R transformation.
Poster Session P4, Data Assimilation
Monday, 6 August 2007, 1:30 PM-3:30 PM, Halls C & D
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