Turkish Journal of Electrical Engineering and Computer Sciences




The rainfall amount observed at a given location mostly depend on the cloud density, which can be quantified with the reflectivity values observed by meteorology weather radars. In this study, we aim to estimate the rainfall amount using a Kalman filter with radar reflectivity measurements. We first assume that the amount of rainfall observed at automatic weather observation stations (AWOSs) are elements of an unknown state vector and consider the Kalman filter process model as the true rainfall amounts observed at these AWOSs over time. For the measurement model of the Kalman filter, we use the radar reflectivity values observed at each AWOS location. For the execution of the Kalman filter, a number of rainfall amount and radar reflectivity value pairs are first required to learn the process and measurement models of the Kalman filter. The estimation performance of the proposed Kalman filter is then compared with empirical reflectivity (Z) - rainfall (R) relationships. Numerical results show that when the Kalman filter is executed with radar reflectivity measurements observed around a large number of AWOS locations, the mean squared errors of the Kalman filter rainfall estimates are smaller than the ones obtained with empirical ZR relationships.


Kalman filter, multivariate least squares, radar reflectivity measurements, rainfall estimation

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