paper
2023
Post-processing & calibration
Extremes & hazards New model or method
CNN / U-Net6 h–3 days Journal
2023-01-01
012
Jan 2023
Omveer Sharma, Dhananjay Trivedi, Sandeep Pattnaik et al.
IEEE Transactions on Geoscience and Remote Sensing
U-Net trained on WRF rainfall outputs cuts district-scale heavy rainfall MAE by 64.78% over Assam and lifts categorical accuracy to 77.9%.
Improvement in District Scale Heavy Rainfall Prediction Over Complex Terrain of North East India Using Deep Learning
In one sentence. U-Net trained on WRF rainfall outputs cuts district-scale heavy rainfall MAE by 64.78% over Assam and lifts categorical accuracy to 77.9%.
Labels
- Topic
- Post-processing & calibration, Extremes & hazards
- Type
- New model or method
- Method
- CNN / U-Net
- Time scale
- 6 h–3 days
- Domain
- Regional
- Data
- NWP analysis or forecasts (IFS, GFS, ...), Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.1109/tgrs.2023.3322676 OpenAlex:W4387449032
- Added
- 2026-10-22
Labels and summary are generated by Claude and reviewed weekly. Wrong? Edit this entry.