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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Latest update: 22 Jul 2024
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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.