A Data-driven Approach to Detecting Precipitation from Meteorological Sensor Data

Abstract

Precipitation is dependent on a myriad of atmospheric conditions. In this paper, we study how certain atmospheric parameters impact the occurrence of rainfall. We propose a data-driven, machine-learning based methodology to detect precipitation using various meteorological sensor data. Our approach achieves a true detection rate of 87.4% and a moderately low false alarm rate of 32.2%.

Published in Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2018