Detection of precipitation and fog using machine learning on backscatter data from lidar ceilometer

Abstract

The lidar ceilometer estimates cloud height by analyzing backscatter data. This study examines weather detectability using a lidar ceilometer by making an unprecedented attempt at detecting weather phenomena through the application of machine learning techniques to the backscatter data obtained from a lidar ceilometer. This study investigates the weather phenomena of precipitation and fog, which are expected to greatly affect backscatter data. In this experiment, the backscatter data obtained from the lidar ceilometer, CL51, installed in Boseong, South Korea, were used. For validation, the data from the automatic weather station for precipitation and visibility sensor PWD20 for fog, installed at the same location, were used. The experimental results showed potential for precipitation detection, which yielded an F1 score of 0.34. However, fog detection was found to be very difficult and yielded an F1 score of 0.10.