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

Related authors

Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks
Young Jun Kim, Hyun-Cheol Kim, Daehyeon Han, Sanggyun Lee, and Jungho Im
The Cryosphere, 14, 1083–1104, https://doi.org/10.5194/tc-14-1083-2020,https://doi.org/10.5194/tc-14-1083-2020, 2020
Short summary

Related subject area

Atmospheric sciences
Exploring the footprint representation of microwave radiance observations in an Arctic limited-area data assimilation system
Máté Mile, Stephanie Guedj, and Roger Randriamampianina
Geosci. Model Dev., 17, 6571–6587, https://doi.org/10.5194/gmd-17-6571-2024,https://doi.org/10.5194/gmd-17-6571-2024, 2024
Short summary
Analysis of model error in forecast errors of extended atmospheric Lorenz 05 systems and the ECMWF system
Hynek Bednář and Holger Kantz
Geosci. Model Dev., 17, 6489–6511, https://doi.org/10.5194/gmd-17-6489-2024,https://doi.org/10.5194/gmd-17-6489-2024, 2024
Short summary
Description and validation of Vehicular Emissions from Road Traffic (VERT) 1.0, an R-based framework for estimating road transport emissions from traffic flows
Giorgio Veratti, Alessandro Bigi, Sergio Teggi, and Grazia Ghermandi
Geosci. Model Dev., 17, 6465–6487, https://doi.org/10.5194/gmd-17-6465-2024,https://doi.org/10.5194/gmd-17-6465-2024, 2024
Short summary
AeroMix v1.0.1: a Python package for modeling aerosol optical properties and mixing states
Sam P. Raj, Puna Ram Sinha, Rohit Srivastava, Srinivas Bikkina, and Damu Bala Subrahamanyam
Geosci. Model Dev., 17, 6379–6399, https://doi.org/10.5194/gmd-17-6379-2024,https://doi.org/10.5194/gmd-17-6379-2024, 2024
Short summary
Impact of ITCZ width on global climate: ITCZ-MIP
Angeline G. Pendergrass, Michael P. Byrne, Oliver Watt-Meyer, Penelope Maher, and Mark J. Webb
Geosci. Model Dev., 17, 6365–6378, https://doi.org/10.5194/gmd-17-6365-2024,https://doi.org/10.5194/gmd-17-6365-2024, 2024
Short summary

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. 
Download
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.