paper
2022
Downscaling & super-resolution
Application study
Classical ML (trees, GP, linear, kernels)Climate projections / centennialTsinghua University (NowcastNet) Journal
2022-09-10
022
Sep 2022
Jing‐Cheng Han, Wenting Zheng, Zhe Liu et al.
Journal of Water Resources Planning and Management
Tsinghua University (NowcastNet)
SVM and MLP downscale GCM output to daily precipitation at 10 Shenzhen stations, outperforming SDSM and projecting ensemble precipitation trends to 2100.
Downscaling & super-resolution
Downscaling of Precipitation for Climate Change Projections Using Multiple Machine Learning Techniques: Case Study of Shenzhen City, China
In one sentence. SVM and MLP downscale GCM output to daily precipitation at 10 Shenzhen stations, outperforming SDSM and projecting ensemble precipitation trends to 2100.
Labels
- Topic
- Downscaling & super-resolution
- Type
- Application study
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- Climate projections / centennial
- Domain
- Local / station
- Data
- CMIP / climate model output, Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.1061/(asce)wr.1943-5452.0001612 OpenAlex:W4295127842
- Added
- 2026-10-22
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