Articles | Volume 17, issue 1
https://doi.org/10.5194/gmd-17-53-2024
© Author(s) 2024. 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-17-53-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model experiment description paper
| 
04 Jan 2024
Model experiment description paper |
| 04 Jan 2024
| 04 Jan 2024
Deep learning model based on multi-scale feature fusion for precipitation nowcasting
Jinkai Tan
Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519080, China
Qiqiao Huang
Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Sheng Chen
CORRESPONDING AUTHOR
Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519080, China
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Cited
19 citations as recorded by crossref.
- A Regional Benchmark for Deep Learning-Based Hourly Precipitation Nowcasting in Latin America A. Almeida et al. https://doi.org/10.1109/ACCESS.2026.3670767
- Improved seasonal precipitation forecasts for the Blue Nile Basin: a deep learning approach R. Wiegels et al. https://doi.org/10.3389/fclim.2026.1691030
- A Hybrid Approach to Physical and Deep Learning Models for Radar-Based Precipitation Nowcasting H. Kim et al. https://doi.org/10.1109/TGRS.2025.3560454
- Improving the fine structure of intense rainfall forecast by a designed generative adversarial network Z. Fang et al. https://doi.org/10.5194/gmd-18-9723-2025
- A Strategy to set up Test Dataset and Evaluation Benchmark for Radar Nowcasting of Precipitation in Italy C. Annella et al. https://doi.org/10.1109/JSTARS.2025.3568185
- Toward Explainable and Transferable Deep Downscaling of Atmospheric Pollutants G. Ashiotis et al. https://doi.org/10.1109/LGRS.2023.3329710
- Oceanic Precipitation Nowcasting Using a UNet-Based Residual and Attention Network and Real-Time Himawari-8 Images X. Ji et al. https://doi.org/10.3390/rs16162871
- RainCast: A Hybrid Precipitation Nowcasting Algorithm Using the Himawari-8/9 Satellite Measurements A. Andreev et al. https://doi.org/10.15622/ia.24.4.4
- TPDTC-Net-DRA: Enhancing Nowcasting of Heavy Precipitation via Dynamic Region Attention X. Qi et al. https://doi.org/10.3390/rs18030490
- START: A Hybrid Spatio-Temporal Attention ResNet Transformer for Explainable Multivariable Meteorological Bias-correction D. Singh et al. https://doi.org/10.1007/s41748-026-01132-4
- Precipitation nowcasting based on convolutional LSTM with spatio-temporal information transformation using multi-meteorological factors D. Liu et al. https://doi.org/10.5194/gmd-19-7089-2026
- PC-BiLSTMNet: A hybrid deep learning model for denoising transient electromagnetic data K. Cheng & X. Wu https://doi.org/10.1016/j.measurement.2024.116494
- Cloud prediction via spatiotemporal-frequency differential and attentional network J. Liu et al. https://doi.org/10.1016/j.engappai.2025.113476
- Multimodal Deep Learning in Precipitation Nowcasting: A Review from a Geometric Evolution S. Fan et al. https://doi.org/10.1080/02564602.2026.2704967
- Real-Time Aircraft Rerouting Optimization in Thunderstorm Environments Leveraging Deep Learning-Based Nowcasting L. Chen et al. https://doi.org/10.3390/aerospace13060545
- PEDNet: A Predictive Encoder–Decoder Network with Multi-Scale Global–Local Modeling for Radar Precipitation Nowcasting Z. Wang et al. https://doi.org/10.3390/atmos17050479
- A multi-task deep learning model for bias correction and merging of precipitation data in the Lancang-Mekong River Basin Y. Jiao et al. https://doi.org/10.1016/j.jhydrol.2025.134026
- Deep Learning Model for Precipitation Nowcasting Based on Residual and Attention Mechanisms Z. Zhang et al. https://doi.org/10.3390/rs17071123
- An Evolution-Unet-ConvNeXt approach based on feature fusion for enhancing the accuracy of short-term precipitation forecasting Y. Su et al. https://doi.org/10.1016/j.atmosres.2025.107984
19 citations as recorded by crossref.
- A Regional Benchmark for Deep Learning-Based Hourly Precipitation Nowcasting in Latin America A. Almeida et al. https://doi.org/10.1109/ACCESS.2026.3670767
- Improved seasonal precipitation forecasts for the Blue Nile Basin: a deep learning approach R. Wiegels et al. https://doi.org/10.3389/fclim.2026.1691030
- A Hybrid Approach to Physical and Deep Learning Models for Radar-Based Precipitation Nowcasting H. Kim et al. https://doi.org/10.1109/TGRS.2025.3560454
- Improving the fine structure of intense rainfall forecast by a designed generative adversarial network Z. Fang et al. https://doi.org/10.5194/gmd-18-9723-2025
- A Strategy to set up Test Dataset and Evaluation Benchmark for Radar Nowcasting of Precipitation in Italy C. Annella et al. https://doi.org/10.1109/JSTARS.2025.3568185
- Toward Explainable and Transferable Deep Downscaling of Atmospheric Pollutants G. Ashiotis et al. https://doi.org/10.1109/LGRS.2023.3329710
- Oceanic Precipitation Nowcasting Using a UNet-Based Residual and Attention Network and Real-Time Himawari-8 Images X. Ji et al. https://doi.org/10.3390/rs16162871
- RainCast: A Hybrid Precipitation Nowcasting Algorithm Using the Himawari-8/9 Satellite Measurements A. Andreev et al. https://doi.org/10.15622/ia.24.4.4
- TPDTC-Net-DRA: Enhancing Nowcasting of Heavy Precipitation via Dynamic Region Attention X. Qi et al. https://doi.org/10.3390/rs18030490
- START: A Hybrid Spatio-Temporal Attention ResNet Transformer for Explainable Multivariable Meteorological Bias-correction D. Singh et al. https://doi.org/10.1007/s41748-026-01132-4
- Precipitation nowcasting based on convolutional LSTM with spatio-temporal information transformation using multi-meteorological factors D. Liu et al. https://doi.org/10.5194/gmd-19-7089-2026
- PC-BiLSTMNet: A hybrid deep learning model for denoising transient electromagnetic data K. Cheng & X. Wu https://doi.org/10.1016/j.measurement.2024.116494
- Cloud prediction via spatiotemporal-frequency differential and attentional network J. Liu et al. https://doi.org/10.1016/j.engappai.2025.113476
- Multimodal Deep Learning in Precipitation Nowcasting: A Review from a Geometric Evolution S. Fan et al. https://doi.org/10.1080/02564602.2026.2704967
- Real-Time Aircraft Rerouting Optimization in Thunderstorm Environments Leveraging Deep Learning-Based Nowcasting L. Chen et al. https://doi.org/10.3390/aerospace13060545
- PEDNet: A Predictive Encoder–Decoder Network with Multi-Scale Global–Local Modeling for Radar Precipitation Nowcasting Z. Wang et al. https://doi.org/10.3390/atmos17050479
- A multi-task deep learning model for bias correction and merging of precipitation data in the Lancang-Mekong River Basin Y. Jiao et al. https://doi.org/10.1016/j.jhydrol.2025.134026
- Deep Learning Model for Precipitation Nowcasting Based on Residual and Attention Mechanisms Z. Zhang et al. https://doi.org/10.3390/rs17071123
- An Evolution-Unet-ConvNeXt approach based on feature fusion for enhancing the accuracy of short-term precipitation forecasting Y. Su et al. https://doi.org/10.1016/j.atmosres.2025.107984
Saved (final revised paper)
Latest update: 15 Aug 2026
Short summary
This study presents a deep learning architecture, multi-scale feature fusion (MFF), to improve the forecast skills of precipitations especially for heavy precipitations. MFF uses multi-scale receptive fields so that the movement features of precipitation systems are well captured. MFF uses the mechanism of discrete probability to reduce uncertainties and forecast errors so that heavy precipitations are produced.
This study presents a deep learning architecture, multi-scale feature fusion (MFF), to improve...
