Articles | Volume 16, issue 20
https://doi.org/10.5194/gmd-16-5895-2023
https://doi.org/10.5194/gmd-16-5895-2023
Model evaluation paper
 | 
20 Oct 2023
Model evaluation paper |  | 20 Oct 2023

Key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learning

Daehyeon Han, Jungho Im, Yeji Shin, and Juhyun Lee

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Cited articles

Adewoyin, R. A., Dueben, P., Watson, P., He, Y., and Dutta, R.: TRU-NET: a deep learning approach to high resolution prediction of rainfall, Mach. Learn., 110, 2035–2062, https://doi.org/10.1007/s10994-021-06022-6, 2021. 
Agrawal, S., Barrington, L., Bromberg, C., Burge, J., Gazen, C., and Hickey, J.: Machine learning for precipitation nowcasting from radar images, arXiv [preprint], arXiv:1912.12132, 2019. 
Albu, A.-I., Czibula, G., Mihai, A., Czibula, I. G., Burcea, S., and Mezghani, A.: NeXtNow: A Convolutional Deep Learning Model for the Prediction of Weather Radar Data for Nowcasting Purposes, Remote Sens.-Basel, 14, 3890, https://doi.org/10.3390/rs14163890, 2022. 
Aswin, S., Geetha, P., and Vinayakumar, R.: Deep learning models for the prediction of rainfall, 2018 International Conference on Communication and Signal Processing (ICCSP), 0657–0661, 2018. 
Ayzel, G.: RainNet: a convolutional neural network for radar-based precipitation nowcasting, GitHub [code], https://github.com/hydrogo/rainnet (last access: 18 September 2023), 2020. 
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Short summary
To identify the key factors affecting quantitative precipitation nowcasting (QPN) using deep learning (DL), we carried out a comprehensive evaluation and analysis. We compared four key factors: DL model, length of the input sequence, loss function, and ensemble approach. Generally, U-Net outperformed ConvLSTM. Loss function and ensemble showed potential for improving performance when they synergized well. The length of the input sequence did not significantly affect the results.
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