paper
2025
Observations & remote sensing
Climate analysis & attribution Position or perspective paper
OtherClimate projections / centennialCalifornia Institute of Technology (Caltech)Columbia University (LEAP, Lamont-Doherty)Lawrence Berkeley National Laboratory / NERSCNASANOAA (incl. GFDL, PSL, GSL, NCEP/EMC) Journal
2025-12-11
055
Dec 2025
Ann M. Fridlind, Gregory S. Elsaesser, Marcus van Lier‐Walqui et al.
Bulletin of the American Meteorological Society
California Institute of Technology (Caltech), Columbia University (LEAP, Lamont-Doherty), Lawrence Berkeley National Laboratory / NERSC, NASA, NOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
Proposes a climate OSSE framework that uses machine learning to calibrate GISS-E3 physics against satellite data to value planetary boundary layer observations.
Toward a Climate OSSE Framework for Satellite Mission Design
In one sentence. Proposes a climate OSSE framework that uses machine learning to calibrate GISS-E3 physics against satellite data to value planetary boundary layer observations.
Labels
- Topic
- Observations & remote sensing, Climate analysis & attribution
- Type
- Position or perspective paper
- Method
- Other
- Time scale
- Climate projections / centennial
- Domain
- Global
- Data
- CMIP / climate model output, Satellite
- Status
- Research
- Identifiers
- DOI:10.1175/bams-d-24-0242.1 OpenAlex:W4417237922
- Added
- 2026-10-22
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