paper
2020
Climate analysis & attribution
Benchmarks, datasets & evaluation New model or method
Classical ML (trees, GP, linear, kernels)Seasonal to annualOak Ridge National Laboratory Journal
2020-04-22
026
Apr 2020
Lu Li, Wei Shangguan, Yi Deng et al.
Journal of Hydrometeorology
Oak Ridge National Laboratory
Random-forest-based Granger causal framework isolates soil moisture to precipitation feedback over the US and uses it to benchmark Earth system models for land-atmosphere coupling.
Climate analysis & attribution
A Causal Inference Model Based on Random Forests to Identify the Effect of Soil Moisture on Precipitation
In one sentence. Random-forest-based Granger causal framework isolates soil moisture to precipitation feedback over the US and uses it to benchmark Earth system models for land-atmosphere coupling.
Labels
- Topic
- Climate analysis & attribution, Benchmarks, datasets & evaluation
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- Seasonal to annual
- Domain
- Regional
- Data
- CMIP / climate model output, Other
- Status
- Research
- Identifiers
- DOI:10.1175/jhm-d-19-0209.1 OpenAlex:W3019284798
- Added
- 2026-10-22
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