paper
2018
Observations & remote sensing
New model or method
Classical ML (trees, GP, linear, kernels)Nanjing University of Information Science & Technology (NUIST)China Meteorological Administration (CMA) Journal
2018-11-05
032
Nov 2018
Min Min, Chen Bai, Jianping Guo et al.
IEEE Transactions on Geoscience and Remote Sensing
Nanjing University of Information Science & Technology (NUIST), China Meteorological Administration (CMA)
Random forest estimates summertime precipitation from Himawari-8 infrared brightness temperatures plus GFS predictors, reaching 0.87 classification accuracy and 2.0 mm/h RMSE against GPM.
Estimating Summertime Precipitation from Himawari-8 and Global Forecast System Based on Machine Learning
In one sentence. Random forest estimates summertime precipitation from Himawari-8 infrared brightness temperatures plus GFS predictors, reaching 0.87 classification accuracy and 2.0 mm/h RMSE against GPM.
Labels
- Topic
- Observations & remote sensing
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Domain
- Regional
- Data
- Satellite, NWP analysis or forecasts (IFS, GFS, ...)
- Status
- Research
- Identifiers
- DOI:10.1109/tgrs.2018.2874950 OpenAlex:W2900303043
- Added
- 2026-10-22
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