Articles | Volume 16, issue 20
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

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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,, 2022. 
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Ayzel, G.: RainNet: a convolutional neural network for radar-based precipitation nowcasting, GitHub [code], (last access: 18 September 2023), 2020. 
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.