paper
2020
Sub-seasonal to seasonal
New model or method
RNN / LSTM / ConvLSTMCNN / U-NetSeasonal to annual Journal
2020-01-01
008
Jan 2020
Hiroki MONDA, Ryo Kaneko, Shiho Onomura et al.
Journal of Japan Society of Civil Engineers Ser B1 (Hydraulic Engineering)
LSTM and CNN predict monthly mean temperature and precipitation from station and sea-surface temperature data, with mixed skill.
ATTEMPT OF ONE-MONTH WEATHER FORECAST USING MACHINE LEARNING
In one sentence. LSTM and CNN predict monthly mean temperature and precipitation from station and sea-surface temperature data, with mixed skill.
Labels
- Topic
- Sub-seasonal to seasonal
- Type
- New model or method
- Method
- RNN / LSTM / ConvLSTM, CNN / U-Net
- Time scale
- Seasonal to annual
- Domain
- Regional
- Data
- Station / in-situ, Other
- Status
- Research
- Identifiers
- DOI:10.2208/jscejhe.76.2_i_331 OpenAlex:W3215458648
- Added
- 2026-10-22
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