paper
2022
Data assimilation
Sub-seasonal to seasonal New model or method
Classical ML (trees, GP, linear, kernels)2 weeks–2 monthsNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)NSF NCAR / UCARUniversity of Washington Journal
2022-05-21
047
May 2022
Gregory J. Hakim, Chris Snyder, Stephen Gregory Penny et al.
Geophysical Research Letters
NOAA (incl. GFDL, PSL, GSL, NCEP/EMC), NSF NCAR / UCAR, University of Washington
Kalman filter with a trained linear inverse model performs strongly coupled assimilation, cutting SST analysis error over 20% and extending forecast skill to 50 days.
Subseasonal Forecast Skill Improvement From Strongly Coupled Data Assimilation With a Linear Inverse Model
In one sentence. Kalman filter with a trained linear inverse model performs strongly coupled assimilation, cutting SST analysis error over 20% and extending forecast skill to 50 days.
Labels
- Topic
- Data assimilation, Sub-seasonal to seasonal
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 2 weeks–2 months
- Domain
- Global
- Data
- ERA5 / reanalysis
- Status
- Research
- Identifiers
- DOI:10.1029/2022gl097996 OpenAlex:W4281262425
- Added
- 2026-10-22
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