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EAFs: A Method for Predicting Short-term Heavy Precipitation with an Attention Feedforward Encoder

The high-precision heavy precipitation forecast 1 to 2 hours in advance can bring huge economic benefits to the relevant departments that rely on weather decision-making in the real society. In order to improve the prediction accuracy of impending heavy precipitation, an Encoder Attention Feedforwards (EAFs) method with an attention feedforward encoder is proposed. This method firstly uses cubic polynomial interpolation method to perform data gridding on the 3-hour and 24-hour transformation, humidity, dew point temperature and other 6 live observation elements of the ground station. Combined with the radar monitoring data, a self-built pre-extractor is used to construct the ground - Radar precipitation fusion data set, and then use the data set as the input of the model for precipitation forecast. In order to explore the prediction potential of the model, the meteorological heavy precipitation TS, false alarm rate, detection rate, mean square error, etc. were selected as the performance indicators for comprehensive evaluation of the EAFs model, and compared with LSTM, ConvLSTM, LSTM-Attention and other models. Experiments show that the proposed method has a strong ability to predict the precipitation in the next hour, which is better than many of the compared models, and its meteorological TS score is improved by at least 10% on the basis of many of the compared models, and the detection rate, false alarm rate and false alarm rate are improved. Both the rate and the mean square error are better than many of the compared models, and it has certain application value for the forecast of short-term heavy precipitation.