Geostationary weather radar super-resolution modelling and reconstruction process

Abstract

The geostationary weather radar reflectivity data of low horizontal resolution would limit its application in the observation of small-scale weather system and increase the errors of rainfall retrieval, therefore, the capability to enhance the horizontal resolution of reflectivity data is of special interest. In this study, a new super-resolution model based on the idea of sub-division in one resolution volume for geostationary weather radar and an oversampling technique along the radar’s spiral scan track are presented. The reconstruction is physically based on the occurrence of multiple partially correlated measurements. Mathematically, the process is equivalent to a linear inversion of ill-posed problem, and its solution is pursued by means of the Tikhonov and truncated singular value decomposition (TSVD) inversion method. Experiment results show that the proposed model and reconstruction process are efficient for the horizontal resolution improvement of reflectivity data, and more fine detail could be present through reconstruction.