Articles | Volume 12, issue 4
https://doi.org/10.5194/gmd-12-1387-2019
© Author(s) 2019. 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-12-1387-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
09 Apr 2019
Model description paper |
| 09 Apr 2019
| 09 Apr 2019
Optical flow models as an open benchmark for radar-based precipitation nowcasting (rainymotion v0.1)
Institute for Environmental Sciences and Geography, University of Potsdam, Potsdam, Germany
Maik Heistermann
Institute for Environmental Sciences and Geography, University of Potsdam, Potsdam, Germany
Tanja Winterrath
Department of Hydrometeorology, Deutscher Wetterdienst, Offenbach, Germany
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Cited
71 citations as recorded by crossref.
- Learning from Precipitation Events in the Wider Domain to Improve the Performance of a Deep Learning–Based Precipitation Nowcasting Model T. Inoue & R. Misumi 10.1175/WAF-D-21-0078.1
- ConvLSTM Network-Based Rainfall Nowcasting Method with Combined Reflectance and Radar-Retrieved Wind Field as Inputs W. Liu et al. 10.3390/atmos13030411
- STUNNER: Radar Echo Extrapolation Model Based on Spatiotemporal Fusion Neural Network W. Fang et al. 10.1109/TGRS.2023.3268187
- Convective Storm VIL and Lightning Nowcasting Using Satellite and Weather Radar Measurements Based on Multi-Task Learning Models Y. Li et al. 10.1007/s00376-022-2082-6
- A Novel Approach for the Global Detection and Nowcasting of Deep Convection and Thunderstorms R. Müller et al. 10.3390/rs14143372
- Large‐Sample Evaluation of Radar Rainfall Nowcasting for Flood Early Warning R. Imhoff et al. 10.1029/2021WR031591
- PFST-LSTM: A SpatioTemporal LSTM Model With Pseudoflow Prediction for Precipitation Nowcasting C. Luo et al. 10.1109/JSTARS.2020.3040648
- Deep Scene Flow Learning: From 2D Images to 3D Point Clouds X. Xiang et al. 10.1109/TPAMI.2023.3319448
- An Long Short-Term Memory Model with Multi-Scale Context Fusion and Attention for Radar Echo Extrapolation G. He et al. 10.3390/rs16020376
- Pysteps: an open-source Python library for probabilistic precipitation nowcasting (v1.0) S. Pulkkinen et al. 10.5194/gmd-12-4185-2019
- Performance Comparison between Deep Learning and Optical Flow-Based Techniques for Nowcast Precipitation from Radar Images M. Marrocu & L. Massidda 10.3390/forecast2020011
- Lagrangian Integro-Difference Equation Model for Precipitation Nowcasting 10.1175/JTECH-D-21-0013.1
- Advection-Free Convolutional Neural Network for Convective Rainfall Nowcasting J. Ritvanen et al. 10.1109/JSTARS.2023.3238016
- Machine Learning for Fog-and-Low-Stratus Nowcasting from Meteosat SEVIRI Satellite Images D. Bari et al. 10.3390/atmos14060953
- Physical‐Dynamic‐Driven AI‐Synthetic Precipitation Nowcasting Using Task‐Segmented Generative Model R. Wang et al. 10.1029/2023GL106084
- EfficientRainNet: Leveraging EfficientNetV2 for memory-efficient rainfall nowcasting M. Sit et al. 10.1016/j.envsoft.2024.106001
- A method of radar echo extrapolation based on dilated convolution and attention convolution X. Shen et al. 10.1038/s41598-022-13969-6
- Two-Stage UA-GAN for Precipitation Nowcasting L. Xu et al. 10.3390/rs14235948
- First Order and Second Order Learning Algorithms on the Special Orthogonal Group to Compute the SVD of Data Matrices S. Fiori et al. 10.3390/electronics9020334
- Synthetic Rain Models and Optical Flow Algorithms for Improving the Resolution of Rain Attenuation Time Series Simulated From Numerical Weather Prediction M. Razavian et al. 10.1029/2022RS007553
- Assessment of deterministic and probabilistic precipitation nowcasting techniques over New York metropolitan area A. Tounsi et al. 10.1016/j.envsoft.2023.105803
- Spatial and Temporal Evaluation of Radar Rainfall Nowcasting Techniques on 1,533 Events R. Imhoff et al. 10.1029/2019WR026723
- Coordinate-Transformed Dynamic Mode Decomposition for Short-Term Rainfall Forecasting S. Zheng et al. 10.1109/TGRS.2024.3383058
- Reliable precipitation nowcasting using probabilistic diffusion models C. Nai et al. 10.1088/1748-9326/ad2891
- MSDM v1.0: A machine learning model for precipitation nowcasting over eastern China using multisource data D. Li et al. 10.5194/gmd-14-4019-2021
- Deep learning model based on multi-scale feature fusion for precipitation nowcasting J. Tan et al. 10.5194/gmd-17-53-2024
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- Deep Vision in Analysis and Recognition of Radar Data: Achievements, Advancements, and Challenges Q. Liu et al. 10.1109/MSMC.2022.3216943
- REMNet: Recurrent Evolution Memory-Aware Network for Accurate Long-Term Weather Radar Echo Extrapolation J. Jing et al. 10.1109/TGRS.2022.3198851
- Use of Deep Learning for Weather Radar Nowcasting J. Cuomo & V. Chandrasekar 10.1175/JTECH-D-21-0012.1
- Motion-Guided Global–Local Aggregation Transformer Network for Precipitation Nowcasting X. Dong et al. 10.1109/TGRS.2022.3217639
- Developing Deep Learning Models for Storm Nowcasting J. Cuomo & V. Chandrasekar 10.1109/TGRS.2021.3110180
- CLGAN: a generative adversarial network (GAN)-based video prediction model for precipitation nowcasting Y. Ji et al. 10.5194/gmd-16-2737-2023
- Enhancing Rainfall Nowcasting Using Generative Deep Learning Model with Multi-Temporal Optical Flow J. Ha & H. Lee 10.3390/rs15215169
- Long lead-time radar rainfall nowcasting method incorporating atmospheric conditions using long short-term memory networks K. Zhu et al. 10.3389/fenvs.2022.1054235
- Research on Radar Echo Extrapolation Method by Fusing Environment Grid Point Field Information Y. Wen et al. 10.3390/atmos14060980
- Convcast: An embedded convolutional LSTM based architecture for precipitation nowcasting using satellite data A. Kumar et al. 10.1371/journal.pone.0230114
- Nationwide Radar-Based Precipitation Nowcasting—A Localization Filtering Approach and its Application for Germany R. Reinoso-Rondinel et al. 10.1109/JSTARS.2022.3144342
- Rad-cGAN v1.0: Radar-based precipitation nowcasting model with conditional generative adversarial networks for multiple dam domains S. Choi & Y. Kim 10.5194/gmd-15-5967-2022
- Improving Nowcasting of Convective Development by Incorporating Polarimetric Radar Variables Into a Deep‐Learning Model X. Pan et al. 10.1029/2021GL095302
- PredRANN: The spatiotemporal attention Convolution Recurrent Neural Network for precipitation nowcasting C. Luo et al. 10.1016/j.knosys.2021.107900
- Scale‐dependent blending of ensemble rainfall nowcasts and numerical weather prediction in the open‐source pysteps library R. Imhoff et al. 10.1002/qj.4461
- MLAM: Multi-Layer Attention Module for Radar Extrapolation Based on Spatiotemporal Sequence Neural Network S. Wang et al. 10.3390/s23198065
- Reconstruction of Missing Data in Weather Radar Image Sequences Using Deep Neuron Networks L. Gao et al. 10.3390/app11041491
- SFTformer: A Spatial-Frequency-Temporal Correlation-Decoupling Transformer for Radar Echo Extrapolation L. Xu et al. 10.1109/TGRS.2024.3367857
- Deep Learning-Based Weather Prediction: A Survey X. Ren et al. 10.1016/j.bdr.2020.100178
- A Radar Reflectivity Image Prediction Method: The Spatial MIM + Pix2Pix J. Guo et al. 10.3390/rs15235554
- Precipitation Nowcasting with Orographic Enhanced Stacked Generalization: Improving Deep Learning Predictions on Extreme Events G. Franch et al. 10.3390/atmos11030267
- Umgang mit Unsicherheiten in der Hochwasservorhersage am Beispiel des Emschergebiets A. Treis et al. 10.1007/s35147-023-1866-7
- Spatiotemporal Optimization for Short-Term Solar Forecasting Based on Satellite Imagery M. Oh et al. 10.3390/en14082216
- Nowcasting Meso-γ-Scale Convective Storms Using Convolutional LSTM Models and High-Resolution Radar Observations D. Kim & T. Ushio 10.16993/tellusa.37
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- A Generative Adversarial and Spatiotemporal Differential Fusion Method in Radar Echo Extrapolation X. Niu et al. 10.3390/rs15225329
- Hydrological application of radar rainfall nowcasting in the Netherlands D. Heuvelink et al. 10.1016/j.envint.2019.105431
- Dynamic Multiscale Fusion Generative Adversarial Network for Radar Image Extrapolation S. Chen et al. 10.1109/TGRS.2022.3193458
- TSRC: A Deep Learning Model for Precipitation Short-Term Forecasting over China Using Radar Echo Data Q. Huang et al. 10.3390/rs15010142
- 3D-UNet-LSTM: A Deep Learning-Based Radar Echo Extrapolation Model for Convective Nowcasting S. Guo et al. 10.3390/rs15061529
- RainNet v1.0: a convolutional neural network for radar-based precipitation nowcasting G. Ayzel et al. 10.5194/gmd-13-2631-2020
- Quantifying the Location Error of Precipitation Nowcasts A. Costa Tomaz de Souza et al. 10.1155/2020/8841913
- Improving precipitation nowcasting using a three-dimensional convolutional neural network model from Multi Parameter Phased Array Weather Radar observations D. Kim et al. 10.1016/j.atmosres.2021.105774
- MSLKNet: A Multi-Scale Large Kernel Convolutional Network for Radar Extrapolation W. Tian et al. 10.3390/atmos15010052
- Three-Dimensional Gridded Radar Echo Extrapolation for Convective Storm Nowcasting Based on 3D-ConvLSTM Model N. Sun et al. 10.3390/rs14174256
- A Precipitation Nowcasting Mechanism for Real-World Data Based on Machine Learning Y. Xiang et al. 10.1155/2020/8408931
- Exploiting radar polarimetry for nowcasting thunderstorm hazards using deep learning N. Rombeek et al. 10.5194/nhess-24-133-2024
- Weather Radar Echo Extrapolation with Dynamic Weight Loss Y. Zhang et al. 10.3390/rs15123138
- Advancing very short-term rainfall prediction with blended U-Net and partial differential approaches J. Ha & J. Park 10.3389/feart.2023.1301523
- TAASRAD19, a high-resolution weather radar reflectivity dataset for precipitation nowcasting G. Franch et al. 10.1038/s41597-020-0574-8
- Coupling a Neural Network with a Spatial Downscaling Procedure to Improve Probabilistic Nowcast for Urban Rain Radars M. Marrocu & L. Massidda 10.3390/forecast4040046
- KI-basiertes Vorhersagemodell für Kürzestfrist-Vorhersagen von Starkregen J. Koltermann da Silva et al. 10.1007/s35147-023-1875-6
- Cloud Nowcasting with Structure-Preserving Convolutional Gated Recurrent Units S. Kellerhals et al. 10.3390/atmos13101632
- A hybrid of RainNet and genetic algorithm in nowcasting prediction T. Ngan et al. 10.1007/s12145-023-01120-6
71 citations as recorded by crossref.
- Learning from Precipitation Events in the Wider Domain to Improve the Performance of a Deep Learning–Based Precipitation Nowcasting Model T. Inoue & R. Misumi 10.1175/WAF-D-21-0078.1
- ConvLSTM Network-Based Rainfall Nowcasting Method with Combined Reflectance and Radar-Retrieved Wind Field as Inputs W. Liu et al. 10.3390/atmos13030411
- STUNNER: Radar Echo Extrapolation Model Based on Spatiotemporal Fusion Neural Network W. Fang et al. 10.1109/TGRS.2023.3268187
- Convective Storm VIL and Lightning Nowcasting Using Satellite and Weather Radar Measurements Based on Multi-Task Learning Models Y. Li et al. 10.1007/s00376-022-2082-6
- A Novel Approach for the Global Detection and Nowcasting of Deep Convection and Thunderstorms R. Müller et al. 10.3390/rs14143372
- Large‐Sample Evaluation of Radar Rainfall Nowcasting for Flood Early Warning R. Imhoff et al. 10.1029/2021WR031591
- PFST-LSTM: A SpatioTemporal LSTM Model With Pseudoflow Prediction for Precipitation Nowcasting C. Luo et al. 10.1109/JSTARS.2020.3040648
- Deep Scene Flow Learning: From 2D Images to 3D Point Clouds X. Xiang et al. 10.1109/TPAMI.2023.3319448
- An Long Short-Term Memory Model with Multi-Scale Context Fusion and Attention for Radar Echo Extrapolation G. He et al. 10.3390/rs16020376
- Pysteps: an open-source Python library for probabilistic precipitation nowcasting (v1.0) S. Pulkkinen et al. 10.5194/gmd-12-4185-2019
- Performance Comparison between Deep Learning and Optical Flow-Based Techniques for Nowcast Precipitation from Radar Images M. Marrocu & L. Massidda 10.3390/forecast2020011
- Lagrangian Integro-Difference Equation Model for Precipitation Nowcasting 10.1175/JTECH-D-21-0013.1
- Advection-Free Convolutional Neural Network for Convective Rainfall Nowcasting J. Ritvanen et al. 10.1109/JSTARS.2023.3238016
- Machine Learning for Fog-and-Low-Stratus Nowcasting from Meteosat SEVIRI Satellite Images D. Bari et al. 10.3390/atmos14060953
- Physical‐Dynamic‐Driven AI‐Synthetic Precipitation Nowcasting Using Task‐Segmented Generative Model R. Wang et al. 10.1029/2023GL106084
- EfficientRainNet: Leveraging EfficientNetV2 for memory-efficient rainfall nowcasting M. Sit et al. 10.1016/j.envsoft.2024.106001
- A method of radar echo extrapolation based on dilated convolution and attention convolution X. Shen et al. 10.1038/s41598-022-13969-6
- Two-Stage UA-GAN for Precipitation Nowcasting L. Xu et al. 10.3390/rs14235948
- First Order and Second Order Learning Algorithms on the Special Orthogonal Group to Compute the SVD of Data Matrices S. Fiori et al. 10.3390/electronics9020334
- Synthetic Rain Models and Optical Flow Algorithms for Improving the Resolution of Rain Attenuation Time Series Simulated From Numerical Weather Prediction M. Razavian et al. 10.1029/2022RS007553
- Assessment of deterministic and probabilistic precipitation nowcasting techniques over New York metropolitan area A. Tounsi et al. 10.1016/j.envsoft.2023.105803
- Spatial and Temporal Evaluation of Radar Rainfall Nowcasting Techniques on 1,533 Events R. Imhoff et al. 10.1029/2019WR026723
- Coordinate-Transformed Dynamic Mode Decomposition for Short-Term Rainfall Forecasting S. Zheng et al. 10.1109/TGRS.2024.3383058
- Reliable precipitation nowcasting using probabilistic diffusion models C. Nai et al. 10.1088/1748-9326/ad2891
- MSDM v1.0: A machine learning model for precipitation nowcasting over eastern China using multisource data D. Li et al. 10.5194/gmd-14-4019-2021
- Deep learning model based on multi-scale feature fusion for precipitation nowcasting J. Tan et al. 10.5194/gmd-17-53-2024
- A Forecast-Refinement Neural Network Based on DyConvGRU and U-Net for Radar Echo Extrapolation J. Yao et al. 10.1109/ACCESS.2023.3280932
- Deep Vision in Analysis and Recognition of Radar Data: Achievements, Advancements, and Challenges Q. Liu et al. 10.1109/MSMC.2022.3216943
- REMNet: Recurrent Evolution Memory-Aware Network for Accurate Long-Term Weather Radar Echo Extrapolation J. Jing et al. 10.1109/TGRS.2022.3198851
- Use of Deep Learning for Weather Radar Nowcasting J. Cuomo & V. Chandrasekar 10.1175/JTECH-D-21-0012.1
- Motion-Guided Global–Local Aggregation Transformer Network for Precipitation Nowcasting X. Dong et al. 10.1109/TGRS.2022.3217639
- Developing Deep Learning Models for Storm Nowcasting J. Cuomo & V. Chandrasekar 10.1109/TGRS.2021.3110180
- CLGAN: a generative adversarial network (GAN)-based video prediction model for precipitation nowcasting Y. Ji et al. 10.5194/gmd-16-2737-2023
- Enhancing Rainfall Nowcasting Using Generative Deep Learning Model with Multi-Temporal Optical Flow J. Ha & H. Lee 10.3390/rs15215169
- Long lead-time radar rainfall nowcasting method incorporating atmospheric conditions using long short-term memory networks K. Zhu et al. 10.3389/fenvs.2022.1054235
- Research on Radar Echo Extrapolation Method by Fusing Environment Grid Point Field Information Y. Wen et al. 10.3390/atmos14060980
- Convcast: An embedded convolutional LSTM based architecture for precipitation nowcasting using satellite data A. Kumar et al. 10.1371/journal.pone.0230114
- Nationwide Radar-Based Precipitation Nowcasting—A Localization Filtering Approach and its Application for Germany R. Reinoso-Rondinel et al. 10.1109/JSTARS.2022.3144342
- Rad-cGAN v1.0: Radar-based precipitation nowcasting model with conditional generative adversarial networks for multiple dam domains S. Choi & Y. Kim 10.5194/gmd-15-5967-2022
- Improving Nowcasting of Convective Development by Incorporating Polarimetric Radar Variables Into a Deep‐Learning Model X. Pan et al. 10.1029/2021GL095302
- PredRANN: The spatiotemporal attention Convolution Recurrent Neural Network for precipitation nowcasting C. Luo et al. 10.1016/j.knosys.2021.107900
- Scale‐dependent blending of ensemble rainfall nowcasts and numerical weather prediction in the open‐source pysteps library R. Imhoff et al. 10.1002/qj.4461
- MLAM: Multi-Layer Attention Module for Radar Extrapolation Based on Spatiotemporal Sequence Neural Network S. Wang et al. 10.3390/s23198065
- Reconstruction of Missing Data in Weather Radar Image Sequences Using Deep Neuron Networks L. Gao et al. 10.3390/app11041491
- SFTformer: A Spatial-Frequency-Temporal Correlation-Decoupling Transformer for Radar Echo Extrapolation L. Xu et al. 10.1109/TGRS.2024.3367857
- Deep Learning-Based Weather Prediction: A Survey X. Ren et al. 10.1016/j.bdr.2020.100178
- A Radar Reflectivity Image Prediction Method: The Spatial MIM + Pix2Pix J. Guo et al. 10.3390/rs15235554
- Precipitation Nowcasting with Orographic Enhanced Stacked Generalization: Improving Deep Learning Predictions on Extreme Events G. Franch et al. 10.3390/atmos11030267
- Umgang mit Unsicherheiten in der Hochwasservorhersage am Beispiel des Emschergebiets A. Treis et al. 10.1007/s35147-023-1866-7
- Spatiotemporal Optimization for Short-Term Solar Forecasting Based on Satellite Imagery M. Oh et al. 10.3390/en14082216
- Nowcasting Meso-γ-Scale Convective Storms Using Convolutional LSTM Models and High-Resolution Radar Observations D. Kim & T. Ushio 10.16993/tellusa.37
- Temperature forecasting by deep learning methods B. Gong et al. 10.5194/gmd-15-8931-2022
- A Generative Adversarial and Spatiotemporal Differential Fusion Method in Radar Echo Extrapolation X. Niu et al. 10.3390/rs15225329
- Hydrological application of radar rainfall nowcasting in the Netherlands D. Heuvelink et al. 10.1016/j.envint.2019.105431
- Dynamic Multiscale Fusion Generative Adversarial Network for Radar Image Extrapolation S. Chen et al. 10.1109/TGRS.2022.3193458
- TSRC: A Deep Learning Model for Precipitation Short-Term Forecasting over China Using Radar Echo Data Q. Huang et al. 10.3390/rs15010142
- 3D-UNet-LSTM: A Deep Learning-Based Radar Echo Extrapolation Model for Convective Nowcasting S. Guo et al. 10.3390/rs15061529
- RainNet v1.0: a convolutional neural network for radar-based precipitation nowcasting G. Ayzel et al. 10.5194/gmd-13-2631-2020
- Quantifying the Location Error of Precipitation Nowcasts A. Costa Tomaz de Souza et al. 10.1155/2020/8841913
- Improving precipitation nowcasting using a three-dimensional convolutional neural network model from Multi Parameter Phased Array Weather Radar observations D. Kim et al. 10.1016/j.atmosres.2021.105774
- MSLKNet: A Multi-Scale Large Kernel Convolutional Network for Radar Extrapolation W. Tian et al. 10.3390/atmos15010052
- Three-Dimensional Gridded Radar Echo Extrapolation for Convective Storm Nowcasting Based on 3D-ConvLSTM Model N. Sun et al. 10.3390/rs14174256
- A Precipitation Nowcasting Mechanism for Real-World Data Based on Machine Learning Y. Xiang et al. 10.1155/2020/8408931
- Exploiting radar polarimetry for nowcasting thunderstorm hazards using deep learning N. Rombeek et al. 10.5194/nhess-24-133-2024
- Weather Radar Echo Extrapolation with Dynamic Weight Loss Y. Zhang et al. 10.3390/rs15123138
- Advancing very short-term rainfall prediction with blended U-Net and partial differential approaches J. Ha & J. Park 10.3389/feart.2023.1301523
- TAASRAD19, a high-resolution weather radar reflectivity dataset for precipitation nowcasting G. Franch et al. 10.1038/s41597-020-0574-8
- Coupling a Neural Network with a Spatial Downscaling Procedure to Improve Probabilistic Nowcast for Urban Rain Radars M. Marrocu & L. Massidda 10.3390/forecast4040046
- KI-basiertes Vorhersagemodell für Kürzestfrist-Vorhersagen von Starkregen J. Koltermann da Silva et al. 10.1007/s35147-023-1875-6
- Cloud Nowcasting with Structure-Preserving Convolutional Gated Recurrent Units S. Kellerhals et al. 10.3390/atmos13101632
- A hybrid of RainNet and genetic algorithm in nowcasting prediction T. Ngan et al. 10.1007/s12145-023-01120-6
Discussed (final revised paper)
Discussed (preprint)
Latest update: 19 Apr 2024
Short summary
How much will it rain within the next hour? To answer this question, we developed rainymotion – an open source Python software library for precipitation nowcasting. In our benchmark experiments, including a state-of-the-art operational model, rainymotion demonstrated its ability to deliver timely and reliable nowcasts for a broad range of rainfall events. This way, rainymotion can serve as a baseline solution in the field of precipitation nowcasting.
How much will it rain within the next hour? To answer this question, we developed rainymotion...
