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Numerical simulations of radar rainfall error propagation
Author(s) -
Sharif Hatim O.,
Ogden Fred L.,
Krajewski Witold F.,
Xue Ming
Publication year - 2002
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 - 10.1029/2001wr000525
Subject(s) - radar , weather radar , environmental science , rain gauge , thunderstorm , meteorology , vflo , convective storm detection , supercell , precipitation , quantitative precipitation estimation , hydrological modelling , remote sensing , quantitative precipitation forecast , runoff model , storm , watershed , geology , computer science , climatology , geography , telecommunications , machine learning
The primary advantage of radar observations of precipitation compared with traditional rain gauge measurements is their high spatial and temporal resolution and large areal coverage. Unfortunately, radar data require vigorous quality control before being converted into precipitation products that can be used as input to hydrologic models. In this study we coupled a physically based atmospheric model of convective rainfall with an active microwave radiative transfer model to simulate radar observation of thunderstorms. We used the atmospheric model to simulate a well‐documented tornadic supercell storm that occurred near Del City, Oklahoma, on 20 May 1977. We then generated radar observations of that storm and used them to evaluate the propagation of radar rainfall errors through distributed hydrologic simulations. This physically based methodology allows us to directly examine the impact of radar rainfall estimation errors on land‐surface hydrologic predictions and to avoid the limitations imposed by the use of rain gauge data. Results indicate that the geometry of the radar beam and coordinate transformations, due to radar‐watershed‐storm orientation, have an effect on radar rainfall estimation and runoff prediction errors. In addition to uncertainty in the radar reflectivity versus rainfall intensity relationship, there are significant range‐dependent and orientation‐related radar rainfall estimation errors that should be quantified in terms of their impact on runoff predictions. Our methodology provides a tool for performing experiments that address some operational issues related to the process of radar rainfall estimation and its uses in hydrologic prediction.