paper
2022
Downscaling & super-resolution
New model or method
CNN / U-NetChina Meteorological Administration (CMA) Journal
2022-01-20
022
Jan 2022
Ruian Tie, Chunxiang Shi, Gang Wan et al.
Journal of Atmospheric and Oceanic Technology
China Meteorological Administration (CMA)
Light-CLDASSD, a lightweight deep model with station data, downscales temperature from 1/16° to 0.01° and beats HRCLDAS by 0.08 °C RMSE.
Downscaling & super-resolution
To Accurately and Lightly Downscale the Temperature Field by Deep Learning
In one sentence. Light-CLDASSD, a lightweight deep model with station data, downscales temperature from 1/16° to 0.01° and beats HRCLDAS by 0.08 °C RMSE.
Labels
- Topic
- Downscaling & super-resolution
- Type
- New model or method
- Method
- CNN / U-Net
- Domain
- Regional
- Data
- Station / in-situ, Other
- Status
- Research
- Identifiers
- DOI:10.1175/jtech-d-21-0099.1 OpenAlex:W4206542980
- Added
- 2026-10-22
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