A Novel Multi-Input Multi-Output Recurrent Neural Network Based on Multimodal Fusion and Spatiotemporal Prediction for 0–4 Hour Precipitation Nowcasting
In one sentence. MFSP-Net, a multi-input recurrent network, fuses precipitation grids, radar echoes and reanalysis for 0–4 h precipitation nowcasting, improving heavy-rain nowcasts over Southeast China.
Abstract
Multi-source meteorological data can reflect the development process of single meteorological elements from different angles. Making full use of multi-source meteorological data is an effective method to improve the performance of weather nowcasting. For precipitation nowcasting, this paper proposes a novel multi-input multi-output recurrent neural network model based on multimodal fusion and spatiotemporal prediction, named MFSP-Net. It uses precipitation grid data, radar echo data, and reanalysis data as input data and simultaneously realizes 0–4 h precipitation amount nowcasting and precipitation intensity nowcasting. MFSP-Net can perform the spatiotemporal-scale fusion of the three sources of input data while retaining the spatiotemporal information flow of them. The multi-task learning strategy is used to train the network. We conduct experiments on the dataset of Southeast China, and the results show that MFSP-Net comprehensively improves the performance of the nowcasting of precipitation amounts. For precipitation intensity nowcasting, MFSP-Net has obvious advantages in heavy precipitation nowcasting and the middle and late stages of nowcasting.
Labels
- Topic
- Nowcasting, Observations & remote sensing
- Type
- New model or method
- Method
- RNN / LSTM / ConvLSTM, CNN / U-Net
- Time scale
- 0–6 h
- Domain
- Regional
- Data
- Radar, NWP analysis or forecasts (IFS, GFS, ...), Other
- Status
- Research
- Identifiers
- DOI:10.3390/atmos12121596 OpenAlex:W3217658907
- Added
- 2026-10-22
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