Articles | Volume 17, issue 1
https://doi.org/10.5194/gmd-17-53-2024
https://doi.org/10.5194/gmd-17-53-2024
Model experiment description paper
 | 
04 Jan 2024
Model experiment description paper |  | 04 Jan 2024

Deep learning model based on multi-scale feature fusion for precipitation nowcasting

Jinkai Tan, Qiqiao Huang, and Sheng Chen

Data sets

Deep learning model based on multi-scale feature fusion for precipitation nowcasting (v1) Jinkai Tan https://doi.org/10.5281/zenodo.8105573

Model code and software

Deep learning model based on multi-scale feature fusion for precipitation nowcasting (v1) Jinkai Tan https://doi.org/10.5281/zenodo.8105573

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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.