Preprints
https://doi.org/10.5194/gmd-2023-187
https://doi.org/10.5194/gmd-2023-187
Submitted as: development and technical paper
 | 
25 Oct 2023
Submitted as: development and technical paper |  | 25 Oct 2023
Status: this preprint has been withdrawn by the authors.

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

Jianbin Zhang, Zhiqiu Gao, Yubin Li, and Yuncong Jiang

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.

This preprint has been withdrawn.

Jianbin Zhang, Zhiqiu Gao, Yubin Li, and Yuncong Jiang

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2023-187', Anonymous Referee #1, 05 Dec 2023
    • CC1: 'Reply on RC1', Yubin Li, 26 Dec 2023
  • RC2: 'Comment on gmd-2023-187', Anonymous Referee #2, 25 Dec 2023
    • CC2: 'Reply on RC2', Yubin Li, 28 Dec 2023
  • AC1: 'Comment on gmd-2023-187', Zhiqiu Gao, 08 Jan 2024

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2023-187', Anonymous Referee #1, 05 Dec 2023
    • CC1: 'Reply on RC1', Yubin Li, 26 Dec 2023
  • RC2: 'Comment on gmd-2023-187', Anonymous Referee #2, 25 Dec 2023
    • CC2: 'Reply on RC2', Yubin Li, 28 Dec 2023
  • AC1: 'Comment on gmd-2023-187', Zhiqiu Gao, 08 Jan 2024
Jianbin Zhang, Zhiqiu Gao, Yubin Li, and Yuncong Jiang
Jianbin Zhang, Zhiqiu Gao, Yubin Li, and Yuncong Jiang

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This preprint has been withdrawn.

Short summary
This study developed a deep learning model called CNN-BiLSTM-AM for convective weather forecasting. The results showed that the CNN-BiLSTM-AM model outperformed traditional machine learning algorithms in predicting convective weather, with higher accuracy as the forecast lead time increased. When compared to subjective forecasts by forecasters, the objective approach of the CNN-BiLSTM-AM model also demonstrated advantages in various metrics.