paper
2021
Observations & remote sensing
Climate analysis & attribution New model or method
Classical ML (trees, GP, linear, kernels)Seasonal to annualMassachusetts Institute of Technology (MIT) Journal
2021-06-18
022
Jun 2021
Christoph Böhm, Jan H. Schween, Mark Reyers et al.
Journal of Applied Meteorology and Climatology
Massachusetts Institute of Technology (MIT)
A neural network on MODIS brightness temperatures detects Atacama Desert fog better than a cloud-height method (Heidke skill 0.56 vs 0.49), enabling a fog frequency climatology.
Towards a climatology of fog frequency in the Atacama Desert via multi-spectral satellite data and machine learning techniques
In one sentence. A neural network on MODIS brightness temperatures detects Atacama Desert fog better than a cloud-height method (Heidke skill 0.56 vs 0.49), enabling a fog frequency climatology.
Labels
- Topic
- Observations & remote sensing, Climate analysis & attribution
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- Seasonal to annual
- Domain
- Regional
- Data
- Satellite, Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.1175/jamc-d-20-0208.1 OpenAlex:W3176287638
- Added
- 2026-10-22
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