evaluationofdifferentmethodstocorrectradardatausing(33页).doc

evaluationofdifferentmethodstocorrectradardatausing(33页).doc

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evaluationofdifferentmethodstocorrectradardatausing(33页)

large sample Evaluation of TWO methods to correct range dependant ERROR for WSR-88D rainfall ESTIMATES Bertrand Vignal and Witold F. Krajewski Iowa Institute of Hydraulic Research The University of Iowa Submitted to Journal of Hydrometeorology July 2000 Corresponding author address: Dr. Bertrand Vignal Iowa Institute of Hydraulic Research 300 South Riverside Drive, Rm. 404 Iowa City, Iowa 52242-1585 E-mail: vignal@iihr.uiowa.edu Abstract The vertical variability of reflectivity is an important source of error that affects a estimation of rainfall quantities by radar. This error can be reduced if the vertical profile of reflectivity (VPR) is known. Different methods are available to determine VPR based on volume scan radar data. We tested two such methods. The first method, used in the Swiss meteorological service, estimates a mean VPR directly from volumetric radar data collected close to the radar. The second method takes into account the spatial variability of reflectivity and relies on solving an inverse problem in determination of the profile. To test these methods we used two years worth of archive level II radar data from the WRS-88D located in Tulsa, Oklahoma, as well as the corresponding rain gauge observations from the Oklahoma Mesonet. The results obtained in comparing rain estimates from radar data corrected for the VPR influence with rain gauge observation show the benefits of the methods but also their limitations. The performance of the two methods is similar but the inverse method consistently provides better results. However, it requires substantially more computational resources for use in operational environment. Introduction Different sources of error affect radar rainfall estimates. These sources of error are well-known (see, for example, Zawadzki 1982; Austin 1987; Joss and Waldvogel 1990; Smith et al. 1996). To derive accurate rainfall estimates from radar measurements for meteorological or hydrological applica

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