Quantitative Comparison of the Spatial Distribution of Radar and Gauge Rainfall Data

Abstract

Abstract The common statement that a rain gauge network usually provides better observation at specific points while weather radar provides more accurate observation of the spatial distribution of rain field over a large area has never been subjected to quantitative evaluation. The aim of this paper is to evaluate the statement by using some statistical criteria. The Monte Carlo simulation experiment, inverse distance weighting (IDW) interpolation method, and cross-validation technique are used to investigate the relation between the accuracy of the interpolated rainfall and the rain gauge density. The radar reflectivity–rainfall intensity (Z–R) relationship is constructed by the least squares fitting method from observation data of radar and rain gauges. The variation in this relationship and the accuracy of the radar rainfall with rain gauge density are evaluated by using the Monte Carlo simulation experiment. Three storm events are selected as the case studies. The obtained results show that the accuracy of interpolated and radar rainfall increases nonlinearly with increasing gauge density. The higher correlation coefficient (γ) value of radar-rainfall estimation, compared to gauge interpolation, especially in the convective storm, proves that radar observation provides a more accurate spatial structure of the rain field than gauge observation does.