Warning Method of Lightning Nowcasting Based on Machine Learning and Single Station Ground Meteorological Elements
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Abstract:
In order to reduce personal casualties and economic losses caused by lightning disasters, a lightning nowcasting method based on the ADASYN-ET model is proposed. Using four single-station ground meteorological elements of temperature, pressure, humidity and wind combined with ADTD ground-flash location information, a lightning nowcasting method based on the ADASYN-ET model is constructed through the combined application of machine learning techniques such as feature engineering, resampling, and cross-validation, which can provide 0 to 30 min advance warning for the 20 km range of weather stations. The validation results show that the warning method applies to all 8 test sites, and the average F1 Score is 0.60, 0.59 and 0.60, 0 to 10 min, 10 to 20 min and 20 to 30 min in advance. The method is flexible compared to other recent warning methods and systems for implementation and can provide references for the work of lightning prevention and disaster reduction.
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Supported by:Beijing E-Tiller Technology Development Co., Ltd.
Supported by:Beijing E-Tiller Technology Development Co., Ltd.