A deep learning method for convective weather forecasting: CNN-BiLSTM-AM (version 1.0)

Jianbin Zhang, Zhiqiu Gao, Yubin Li, Yuncong Jiang

Preprint · 25 October 2023 · Institute of Atmospheric Physics, Chinese Academy of Sciences, Nanjing University of Information Science & Technology (NUIST)

Paper

In one sentence. CNN-BiLSTM-attention model forecasts convective and heavy precipitation from GFS data, reaching threat score 0.336 versus 0.252 for forecasters.

Abstract

Abstract. This work developed a CNN-BiLSTM-AM model for convective weather forecasting using deep learning algorithms based on reanalysis and forecast data from the NCEP GFS, the performance of the model was evaluated. The results show that: (1) Compared to traditional machine learning algorithms, the CNN-BiLSTM-AM model has the ability to automatically learn deeper nonlinear features of convective weather. As a result, it exhibits higher forecasting accuracy on the convective weather dataset. Furthermore, as the forecast lead time increases, the information value provided by this model also changes. (2) In comparison to subjective forecasts by forecasters, the objective forecasting approach of the CNN-BiLSTM-AM model demonstrates advantages in metrics such as Probability of Detection (POD), False Alarm Rate (FAR), Threat Score (TS), and Missing Alarm Rate (MAR). Specifically, the average TS score for heavy precipitation reaches 0.336, which is a 33.2 % improvement compared to the forecaster's score of 0.252. Moreover, due to the CNN-BiLSTM-AM model's ability to automatically extract classification features based on a large sample dataset and consider a comprehensive range of convective parameters, it effectively reduces the FAR. (3) The interpretability study of the machine learning-based convective weather mechanism reveals that the importance ranking of convective weather forecasting factors arranged by machine learning methods largely aligns with the subjective understanding of forecasters. For example, the total precipitable water (PWAT) is identified as a critical factor for short-term heavy precipitation forecasting, regional factors have significant impacts on convective weather, and vertical motion at 300 hPa provides dynamic lifting conditions for convection. This objective analysis of factor ranking not only further confirms the effectiveness of machine learning in automatically extracting convective weather features but also validates the rationality of the sample set construction. Overall, the use of the CNN-BiLSTM-AM model in convective weather forecasting demonstrates superior performance compared to traditional machine learning algorithms and subjective forecasting methods.

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