Rainfall Estimation from Ground Radar Measurements Using Least Squares Approximations

Rainfall observed on the ground is dependent on the four dimensional radar observations. least squares approximations (lsa) method is a parametric method that is going to be used. the performance of the lsa based rainfall estimation is subject to many factors such as the representativeness and sufficiency of the dataset, the seasonal changes, and regional changes. the goal of the present paper is to estimate rainfall based on adaptive empirical formulas using radar reflectivity and rain measurements. data from melbourne-florida nexrad ground radar (kmlb) and houston –texas nexrad radar (khgx) over different years along with rain gauge measurements are used to develop this method. the nonlinear relationship is derived directly from a dataset consisting of radar measurements and rain gauge measurements. a direct gauge comparison study is done to demonstrate the improvement brought in by the method of least squares approximations to show the feasibility of this system in real life.