Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 19, issue 15
Articles | Volume 19, issue 15
https://doi.org/10.5194/gmd-19-7089-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-7089-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 15
Model experiment description paper
 | 
03 Aug 2026
Model experiment description paper |  | 03 Aug 2026

Precipitation nowcasting based on convolutional LSTM with spatio-temporal information transformation using multi-meteorological factors

Dufu Liu, Feihu Huang, Peng Zheng, Xiaomeng Huang, Xi Wu, Xia Yuan, Jiafeng Zheng, Xiaojie Li, and Jing Hu

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Short summary
Due to the limitations of past data-based models and the high cost of numerical weather prediction computing, accurately forecasting precipitation proximity remains challenging. A dual encoder-decoder framework is proposed to enhance short-term forecasting and reduce underestimation in extreme precipitation by using spatio-temporal information conversion equations and adaptive weighted gradient loss. Demonstrates better accuracy than existing deep learning methods in precipitation datasets.
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