A machine learning based approach to weather parameter estimation in Doppler weather radar

An observed signal of the Doppler weather radar includes not only weather echoes but also a ground clutter. For accurate observation of weather data, we need to remove the effect of the ground clutter. In this paper, we propose to model the spectrum of an observed IQ signal as a mixture density function. To estimate the parameters of the density function, we apply the expectation-maximization (EM) algorithm in a maximum a posteriori (MAP) estimation with hyper parameters learned from the actual measurements of the ground clutter. Experimental results show that the proposed method works well in estimating the wind velocity, rainfall amount, and turbulence from the weather echo even when the spectrum of the weather echo is overlapped with that of the ground clutter in a lower frequency band.