Articles | Volume 13, issue 6
https://doi.org/10.5194/gmd-13-2631-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-2631-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| Highlight paper
| 
11 Jun 2020
Model description paper | Highlight paper |
| 11 Jun 2020
| 11 Jun 2020
RainNet v1.0: a convolutional neural network for radar-based precipitation nowcasting
Institute for Environmental Sciences and Geography, University of Potsdam, Potsdam, Germany
Tobias Scheffer
Department of Computer Science, University of Potsdam, Potsdam, Germany
Maik Heistermann
Institute for Environmental Sciences and Geography, University of Potsdam, Potsdam, Germany
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- Multivariate Upstream Kuroshio Transport (UKT) Prediction and Targeted Observation Sensitive Area Identification of UKT Seasonal Reduction B. Mu et al. 10.1016/j.ocemod.2024.102344
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- A Precipitation Nowcasting Mechanism for Real-World Data Based on Machine Learning Y. Xiang et al. 10.1155/2020/8408931
- Key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learning D. Han et al. 10.5194/gmd-16-5895-2023
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- Advancing very short-term rainfall prediction with blended U-Net and partial differential approaches J. Ha & J. Park 10.3389/feart.2023.1301523
- Evaluating pySTEPS optical flow algorithms for convection nowcasting over the Maritime Continent using satellite data J. Smith et al. 10.5194/nhess-24-567-2024
- Precipitation nowcasting using transformer-based generative models and transfer learning for improved disaster preparedness M. Piran et al. 10.1016/j.jag.2024.103962
- Contextualizing predictive minds M. Butz et al. 10.1016/j.neubiorev.2024.105948
- Three-Dimensional Gridded Radar Echo Extrapolation for Convective Storm Nowcasting Based on 3D-ConvLSTM Model N. Sun et al. 10.3390/rs14174256
- Quantifying the Location Error of Precipitation Nowcasts A. Costa Tomaz de Souza et al. 10.1155/2020/8841913
- Multi-Level Circulation Pattern Classification Based on the Transfer Learning CNN Network Y. Liu et al. 10.3390/atmos13111861
- Skilful nowcasting of extreme precipitation with NowcastNet Y. Zhang et al. 10.1038/s41586-023-06184-4
- Improving radar-based rainfall nowcasting by a nearest-neighbour approach – Part 1: Storm characteristics B. Shehu & U. Haberlandt 10.5194/hess-26-1631-2022
- Deep Learning-Based Radar Composite Reflectivity Factor Estimations from Fengyun-4A Geostationary Satellite Observations F. Sun et al. 10.3390/rs13112229
- How artificial intelligence is transforming weather forecasting for the future J. Huang & B. Chen 10.1360/TB-2024-0100
- HyPhAICC v1.0: a hybrid physics–AI approach for probability fields advection shown through an application to cloud cover nowcasting R. El Montassir et al. 10.5194/gmd-17-6657-2024
- TempNet – temporal super-resolution of radar rainfall products with residual CNNs M. Sit et al. 10.2166/hydro.2023.196
- EfficientRainNet: Leveraging EfficientNetV2 for memory-efficient rainfall nowcasting M. Sit et al. 10.1016/j.envsoft.2024.106001
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Latest update: 13 Dec 2024
Short summary
In this study, we present RainNet, a deep convolutional neural network for radar-based precipitation nowcasting, which was trained to predict continuous precipitation intensities at a lead time of 5 min. RainNet significantly outperformed the benchmark models at all lead times up to 60 min. Yet, an undesirable property of RainNet predictions is the level of spatial smoothing. Obviously, RainNet learned an optimal level of smoothing to produce a nowcast at 5 min lead time.
In this study, we present RainNet, a deep convolutional neural network for radar-based...
