paper
2023
Sub-seasonal to seasonal
Extremes & hazards New model or method
CNN / U-NetRNN / LSTM / ConvLSTM2 weeks–2 monthsNanjing University of Information Science & Technology (NUIST)China Meteorological Administration (CMA) Journal
2023-01-06
026
Jan 2023
Jiehong Xie, Pang‐Chi Hsu, Yamin Hu et al.
Weather and Forecasting
Nanjing University of Information Science & Technology (NUIST), China Meteorological Administration (CMA)
CNN and GRU models forecast East China rainfall anomalies at 10–30 day leads, beating CMA and ECMWF dynamical models in skill and extreme-event scores.
Skillful Extended-Range Forecast of Rainfall and Extreme Events in East China Based on Deep Learning
In one sentence. CNN and GRU models forecast East China rainfall anomalies at 10–30 day leads, beating CMA and ECMWF dynamical models in skill and extreme-event scores.
Labels
- Topic
- Sub-seasonal to seasonal, Extremes & hazards
- Type
- New model or method
- Method
- CNN / U-Net, RNN / LSTM / ConvLSTM
- Time scale
- 2 weeks–2 months
- Domain
- Regional
- Data
- NWP analysis or forecasts (IFS, GFS, ...), ERA5 / reanalysis
- Status
- Research
- Identifiers
- DOI:10.1175/waf-d-22-0132.1 OpenAlex:W4313597868
- Added
- 2026-10-22
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