Articles | Volume 14, issue 6
https://doi.org/10.5194/gmd-14-4019-2021
© Author(s) 2021. 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-14-4019-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
29 Jun 2021
Model description paper |
| 29 Jun 2021
| 29 Jun 2021
MSDM v1.0: A machine learning model for precipitation nowcasting over eastern China using multisource data
Institute of Meteorology and Oceanography, National University of Defense Technology, Changsha 410003, China
Institute of Meteorology and Oceanography, National University of Defense Technology, Changsha 410003, China
Chaohui Chen
Institute of Meteorology and Oceanography, National University of Defense Technology, Changsha 410003, China
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Cited
17 citations as recorded by crossref.
- 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 SPATIOTEMPORAL-AWARE WEIGHTING SCHEME FOR IMPROVING CLIMATE MODEL ENSEMBLE PREDICTIONS M. Fan et al. 10.1615/JMachLearnModelComput.2022046715
- Integrating ANFIS and Qt Framework to Develop a Mobile-Based Typhoon Rainfall Forecasting System S. Lin et al. 10.1155/2022/5248308
- Deep learning for precipitation nowcasting: A survey from the perspective of time series forecasting S. An et al. 10.1016/j.eswa.2024.126301
- Artificial intelligence and numerical weather prediction models: A technical survey M. Waqas et al. 10.1016/j.nhres.2024.11.004
- RAP-Net: Region Attention Predictive Network for precipitation nowcasting Z. Zhang et al. 10.5194/gmd-15-5407-2022
- Enhancing Radar Echo Extrapolation by ConvLSTM2D for Precipitation Nowcasting F. Naz et al. 10.3390/s24020459
- TempEE: Temporal–Spatial Parallel Transformer for Radar Echo Extrapolation Beyond Autoregression S. Chen et al. 10.1109/TGRS.2023.3311510
- Precipitation Nowcasting Based on Deep Learning over Guizhou, China D. Kong et al. 10.3390/atmos14050807
- NLED: Nonlocal Echo Dynamics Network for Radar Echo Extrapolation T. Chen et al. 10.1109/TGRS.2022.3197833
- TSRC: A Deep Learning Model for Precipitation Short-Term Forecasting over China Using Radar Echo Data Q. Huang et al. 10.3390/rs15010142
- Perspectives and challenges on the interaction between tropical cyclone precipitation and the ocean: A review J. Huang et al. 10.1016/j.aosl.2024.100573
- Nowcast for cloud top height from Himawari‐8 data based on deep learning algorithms Z. Yu et al. 10.1002/met.2130
- LPT-QPN: A Lightweight Physics-Informed Transformer for Quantitative Precipitation Nowcasting D. Li et al. 10.1109/TGRS.2023.3328945
- CLGAN: a generative adversarial network (GAN)-based video prediction model for precipitation nowcasting Y. Ji et al. 10.5194/gmd-16-2737-2023
- RainPredRNN: A New Approach for Precipitation Nowcasting with Weather Radar Echo Images Based on Deep Learning D. Tuyen et al. 10.3390/axioms11030107
17 citations as recorded by crossref.
- 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 SPATIOTEMPORAL-AWARE WEIGHTING SCHEME FOR IMPROVING CLIMATE MODEL ENSEMBLE PREDICTIONS M. Fan et al. 10.1615/JMachLearnModelComput.2022046715
- Integrating ANFIS and Qt Framework to Develop a Mobile-Based Typhoon Rainfall Forecasting System S. Lin et al. 10.1155/2022/5248308
- Deep learning for precipitation nowcasting: A survey from the perspective of time series forecasting S. An et al. 10.1016/j.eswa.2024.126301
- Artificial intelligence and numerical weather prediction models: A technical survey M. Waqas et al. 10.1016/j.nhres.2024.11.004
- RAP-Net: Region Attention Predictive Network for precipitation nowcasting Z. Zhang et al. 10.5194/gmd-15-5407-2022
- Enhancing Radar Echo Extrapolation by ConvLSTM2D for Precipitation Nowcasting F. Naz et al. 10.3390/s24020459
- TempEE: Temporal–Spatial Parallel Transformer for Radar Echo Extrapolation Beyond Autoregression S. Chen et al. 10.1109/TGRS.2023.3311510
- Precipitation Nowcasting Based on Deep Learning over Guizhou, China D. Kong et al. 10.3390/atmos14050807
- NLED: Nonlocal Echo Dynamics Network for Radar Echo Extrapolation T. Chen et al. 10.1109/TGRS.2022.3197833
- TSRC: A Deep Learning Model for Precipitation Short-Term Forecasting over China Using Radar Echo Data Q. Huang et al. 10.3390/rs15010142
- Perspectives and challenges on the interaction between tropical cyclone precipitation and the ocean: A review J. Huang et al. 10.1016/j.aosl.2024.100573
- Nowcast for cloud top height from Himawari‐8 data based on deep learning algorithms Z. Yu et al. 10.1002/met.2130
- LPT-QPN: A Lightweight Physics-Informed Transformer for Quantitative Precipitation Nowcasting D. Li et al. 10.1109/TGRS.2023.3328945
- CLGAN: a generative adversarial network (GAN)-based video prediction model for precipitation nowcasting Y. Ji et al. 10.5194/gmd-16-2737-2023
- RainPredRNN: A New Approach for Precipitation Nowcasting with Weather Radar Echo Images Based on Deep Learning D. Tuyen et al. 10.3390/axioms11030107
Latest update: 21 Jan 2025
Short summary
In the daily weather forecast business, numerical weather prediction is mainly used to forecast precipitation, but its performance for nowcasting tasks within 0–2 h is very poor. Hence, we hope to use machine learning to improve the accuracy and resolution of quantitative precipitation nowcasting (QPN) tasks. Previous works focused on the extrapolation of radar echo without using abundant meteorological data. Therefore, we designed a model using three kinds of data for QPN in eastern china.
In the daily weather forecast business, numerical weather prediction is mainly used to forecast...
