paper
2022
Data assimilation
Sub-seasonal to seasonal New model or method
Classical ML (trees, GP, linear, kernels)2 weeks–2 monthsNSF NCAR / UCARNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)University of Washington Journal
2022-05-21
055
May 2022
Gregory J. Hakim, Chris Snyder, Stephen Gregory Penny et al.
Geophysical Research Letters
NSF NCAR / UCAR, NOAA (incl. GFDL, PSL, GSL, NCEP/EMC), 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
Labels and summary are generated by Claude and reviewed weekly. Wrong? Edit this entry.