Research of Lightning Forecasting Based on Deep Learning Model with Radar Reflectivity Factors and Lightning Location Data
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Abstract:
In this paper, the convolutional neural network and gated recurrent units neural network are used to conduct lightning forecasting research based on radar reflectivity factors and lightning location data. First, a deep learning model (Attention-ConvGRU) based on the convolutional neural network and gated recurrent unit neural network that introduces the attention mechanism is constructed. Then, the radar reflectivity factor data and the lightning location data of the corresponding period (6 minutes) are processed into image data, and input into the deep learning model to train the models that can predict lightning, including three models: single lightning data model, single radar data model and lightningradar dual data model. Finally, forecasting experiment and quantitative evaluation are carried out. The comprehensive evaluation shows that the forecasting model has a comprehensive forecasting accuracy of 96.74%, a false alarm rate of 35.83%, and a Critical Success Index (CSI) of 0.2072. The case study shows that the forecasting model has better lightning forecasting skills for thunderstorms with obvious moving trends (type A thunderstorms) than those without obvious moving trends (type B thunderstorms), and the forecasting skill of the model gradually weakens as the intensity of type B thunderstorms weakens.
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Supported by:Beijing E-Tiller Technology Development Co., Ltd.
Supported by:Beijing E-Tiller Technology Development Co., Ltd.