paper
2019
Downscaling & super-resolution
New model or method
RNN / LSTM / ConvLSTMClimate projections / centennialTechnical University of Munich (TUM) Journal
2019-03-09
022
Mar 2019
Duong Tran Anh, Song Pham Van, Thanh Duc Dang et al.
International Journal of Climatology
Technical University of Munich (TUM)
LSTM and feedforward networks downscale CMIP5 precipitation for the Mekong Delta, projecting 20-25% wet-season increases and dry-season decreases for 2036-2065.
Downscaling & super-resolution
Downscaling rainfall using deep learning long short‐term memory and feedforward neural network
In one sentence. LSTM and feedforward networks downscale CMIP5 precipitation for the Mekong Delta, projecting 20-25% wet-season increases and dry-season decreases for 2036-2065.
Labels
- Topic
- Downscaling & super-resolution
- Type
- New model or method
- Method
- RNN / LSTM / ConvLSTM
- Time scale
- Climate projections / centennial
- Domain
- Local / station
- Data
- CMIP / climate model output, Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.1002/joc.6066 OpenAlex:W2921706503
- Added
- 2026-10-22
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