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.
Highlights
The lidar ceilometer is a remote observation device used to measure cloud height at the location in which it is installed
To detect the aforementioned weather phenomena from the backscatter data obtained from the lidar ceilometer, three machine learning models: random forest, support vector machine, and artificial neural network were applied
We made the first attempt to detect weather phenomena using raw backscatter data obtained from a lidar ceilometer
Summary
The lidar ceilometer is a remote observation device used to measure cloud height at the location in which it is installed. Backscatter data from a lidar ceilometer were used primarily for PBLH measurements, but recently they have been used for radiation fog alerts [8], optical aerosol characterization [9], aerosol dispersion simulation [10], and studies of the relationship between cloud occurrence and precipitation [11]. Machine learning techniques have been actively applied to the meteorology field in recent years They are used for forecasting very short-range heavy precipitation [12,13], quality control [14,15] and correction [15,16,17,18] of observed weather data, and predicting winter precipitation types [19]. To detect the aforementioned weather phenomena (precipitation and fog) from the backscatter data obtained from the lidar ceilometer, three machine learning models: random forest, support vector machine, and artificial neural network were applied.
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