paper
2020
Post-processing & calibration
New model or method
Classical ML (trees, GP, linear, kernels)6 h–3 daysChina Meteorological Administration (CMA) Journal
2020-02-18
022
Feb 2020
Xuebing Yang, Yajing Wu, Wensheng Zhang et al.
Intelligent Data Analysis
China Meteorological Administration (CMA)
Introduces Label Distribution Learning with Climate Probability to post-process ensemble precipitation forecasts in East China, improving RMSE and CRPS over baselines.
Label distribution learning with climate probability for ensemble forecasting
In one sentence. Introduces Label Distribution Learning with Climate Probability to post-process ensemble precipitation forecasts in East China, improving RMSE and CRPS over baselines.
Labels
- Topic
- Post-processing & calibration
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 6 h–3 days
- Domain
- Regional
- Data
- NWP analysis or forecasts (IFS, GFS, ...)
- Status
- Research
- Identifiers
- DOI:10.3233/ida-184446 OpenAlex:W3008632533
- Added
- 2026-10-22
Labels and summary are generated by Claude and reviewed weekly. Wrong? Edit this entry.