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
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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Cited
26 citations as recorded by crossref.
- RAIN-F+: The Data-Driven Precipitation Prediction Model for Integrated Weather Observations Y. Choi et al. 10.3390/rs13183627
- Towards a More Realistic and Detailed Deep-Learning-Based Radar Echo Extrapolation Method Y. Hu et al. 10.3390/rs14010024
- HPC cluster-based user-defined data integration platform for deep learning in geoscience applications G. Li & Y. Choi 10.1016/j.cageo.2021.104868
- Real-Time Tephra Detection and Dispersal Forecasting by a Ground-Based Weather Radar M. Syarifuddin et al. 10.3390/rs13245174
- GAN–argcPredNet v1.0: a generative adversarial model for radar echo extrapolation based on convolutional recurrent units K. Zheng et al. 10.5194/gmd-15-1467-2022
- ConvLSTM Network-Based Rainfall Nowcasting Method with Combined Reflectance and Radar-Retrieved Wind Field as Inputs W. Liu et al. 10.3390/atmos13030411
- Performance Comparison between Deep Learning and Optical Flow-Based Techniques for Nowcast Precipitation from Radar Images M. Marrocu & L. Massidda 10.3390/forecast2020011
- RainPredRNN: A New Approach for Precipitation Nowcasting with Weather Radar Echo Images Based on Deep Learning D. Tuyen et al. 10.3390/axioms11030107
- SOPNet Method for the Fine-Grained Measurement and Prediction of Precipitation Intensity Using Outdoor Surveillance Cameras C. Lin et al. 10.1109/ACCESS.2020.3032430
- A Precipitation Nowcasting Mechanism for Real-World Data Based on Machine Learning Y. Xiang et al. 10.1155/2020/8408931
- Near real-time hurricane rainfall forecasting using convolutional neural network models with Integrated Multi-satellitE Retrievals for GPM (IMERG) product T. Kim et al. 10.1016/j.atmosres.2022.106037
- Use of Deep Learning for Weather Radar Nowcasting J. Cuomo & V. Chandrasekar 10.1175/JTECH-D-21-0012.1
- Effective training strategies for deep-learning-based precipitation nowcasting and estimation J. Ko et al. 10.1016/j.cageo.2022.105072
- Enhancing the Encoding-Forecasting Model for Precipitation Nowcasting by Putting High Emphasis on the Latest Data of the Time Step C. Jeong et al. 10.3390/atmos12020261
- Developing Deep Learning Models for Storm Nowcasting J. Cuomo & V. Chandrasekar 10.1109/TGRS.2021.3110180
- EuLerian Identification of ascending AirStreams (ELIAS 2.0) in numerical weather prediction and climate models – Part 1: Development of deep learning model J. Quinting & C. Grams 10.5194/gmd-15-715-2022
- SF-CNN: Signal Filtering Convolutional Neural Network for Precipitation Intensity Estimation C. Lin et al. 10.3390/s22020551
- Quantifying the Location Error of Precipitation Nowcasts A. Costa Tomaz de Souza et al. 10.1155/2020/8841913
- Deep Learning-Based Radar Composite Reflectivity Factor Estimations from Fengyun-4A Geostationary Satellite Observations F. Sun et al. 10.3390/rs13112229
- Nowcasting thunderstorm hazards using machine learning: the impact of data sources on performance J. Leinonen et al. 10.5194/nhess-22-577-2022
- Skilful precipitation nowcasting using deep generative models of radar S. Ravuri et al. 10.1038/s41586-021-03854-z
- Probabilistic Attenuation Nowcasting for the 5G Telecommunication Networks J. Pudashine et al. 10.1109/LAWP.2021.3068393
- DeePS at: A deep learning model for prediction of satellite images for nowcasting purposes V. Ionescu et al. 10.1016/j.procs.2021.08.064
- Very Short-term Prediction of Weather Radar-Based Rainfall Distribution and Intensity Over the Korean Peninsula Using Convolutional Long Short-Term Memory Network Y. Kim & S. Hong 10.1007/s13143-022-00269-2
- Reconstruction of Missing Data in Weather Radar Image Sequences Using Deep Neuron Networks L. Gao et al. 10.3390/app11041491
- RainNet v1.0: a convolutional neural network for radar-based precipitation nowcasting G. Ayzel et al. 10.5194/gmd-13-2631-2020
25 citations as recorded by crossref.
- RAIN-F+: The Data-Driven Precipitation Prediction Model for Integrated Weather Observations Y. Choi et al. 10.3390/rs13183627
- Towards a More Realistic and Detailed Deep-Learning-Based Radar Echo Extrapolation Method Y. Hu et al. 10.3390/rs14010024
- HPC cluster-based user-defined data integration platform for deep learning in geoscience applications G. Li & Y. Choi 10.1016/j.cageo.2021.104868
- Real-Time Tephra Detection and Dispersal Forecasting by a Ground-Based Weather Radar M. Syarifuddin et al. 10.3390/rs13245174
- GAN–argcPredNet v1.0: a generative adversarial model for radar echo extrapolation based on convolutional recurrent units K. Zheng et al. 10.5194/gmd-15-1467-2022
- ConvLSTM Network-Based Rainfall Nowcasting Method with Combined Reflectance and Radar-Retrieved Wind Field as Inputs W. Liu et al. 10.3390/atmos13030411
- Performance Comparison between Deep Learning and Optical Flow-Based Techniques for Nowcast Precipitation from Radar Images M. Marrocu & L. Massidda 10.3390/forecast2020011
- RainPredRNN: A New Approach for Precipitation Nowcasting with Weather Radar Echo Images Based on Deep Learning D. Tuyen et al. 10.3390/axioms11030107
- SOPNet Method for the Fine-Grained Measurement and Prediction of Precipitation Intensity Using Outdoor Surveillance Cameras C. Lin et al. 10.1109/ACCESS.2020.3032430
- A Precipitation Nowcasting Mechanism for Real-World Data Based on Machine Learning Y. Xiang et al. 10.1155/2020/8408931
- Near real-time hurricane rainfall forecasting using convolutional neural network models with Integrated Multi-satellitE Retrievals for GPM (IMERG) product T. Kim et al. 10.1016/j.atmosres.2022.106037
- Use of Deep Learning for Weather Radar Nowcasting J. Cuomo & V. Chandrasekar 10.1175/JTECH-D-21-0012.1
- Effective training strategies for deep-learning-based precipitation nowcasting and estimation J. Ko et al. 10.1016/j.cageo.2022.105072
- Enhancing the Encoding-Forecasting Model for Precipitation Nowcasting by Putting High Emphasis on the Latest Data of the Time Step C. Jeong et al. 10.3390/atmos12020261
- Developing Deep Learning Models for Storm Nowcasting J. Cuomo & V. Chandrasekar 10.1109/TGRS.2021.3110180
- EuLerian Identification of ascending AirStreams (ELIAS 2.0) in numerical weather prediction and climate models – Part 1: Development of deep learning model J. Quinting & C. Grams 10.5194/gmd-15-715-2022
- SF-CNN: Signal Filtering Convolutional Neural Network for Precipitation Intensity Estimation C. Lin et al. 10.3390/s22020551
- Quantifying the Location Error of Precipitation Nowcasts A. Costa Tomaz de Souza et al. 10.1155/2020/8841913
- Deep Learning-Based Radar Composite Reflectivity Factor Estimations from Fengyun-4A Geostationary Satellite Observations F. Sun et al. 10.3390/rs13112229
- Nowcasting thunderstorm hazards using machine learning: the impact of data sources on performance J. Leinonen et al. 10.5194/nhess-22-577-2022
- Skilful precipitation nowcasting using deep generative models of radar S. Ravuri et al. 10.1038/s41586-021-03854-z
- Probabilistic Attenuation Nowcasting for the 5G Telecommunication Networks J. Pudashine et al. 10.1109/LAWP.2021.3068393
- DeePS at: A deep learning model for prediction of satellite images for nowcasting purposes V. Ionescu et al. 10.1016/j.procs.2021.08.064
- Very Short-term Prediction of Weather Radar-Based Rainfall Distribution and Intensity Over the Korean Peninsula Using Convolutional Long Short-Term Memory Network Y. Kim & S. Hong 10.1007/s13143-022-00269-2
- Reconstruction of Missing Data in Weather Radar Image Sequences Using Deep Neuron Networks L. Gao et al. 10.3390/app11041491
1 citations as recorded by crossref.
Latest update: 01 Jun 2023
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...
