paper
2020
Post-processing & calibration
New model or method
Classical ML (trees, GP, linear, kernels) Journal
2020-01-01
008
Jan 2020
Keach Murakami, Tomoyoshi Hirota, Seiji Shimoda et al.
Journal of Agricultural Meteorology
Multimodal machine learning corrects 1 km gridded winter air temperature biases at 88 Hokkaido sites, reducing RMSE by about 0.1°C with a nested model.
Bias correction for spatially interpolated daily mean air temperature during winter in eastern Hokkaido using multimodal machine learning
In one sentence. Multimodal machine learning corrects 1 km gridded winter air temperature biases at 88 Hokkaido sites, reducing RMSE by about 0.1°C with a nested model.
Labels
- Topic
- Post-processing & calibration
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Domain
- Local / station
- Data
- Station / in-situ, Other
- Status
- Research
- Identifiers
- DOI:10.2480/agrmet.d-20-00027 OpenAlex:W3092367252
- Added
- 2026-10-22
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