paper
2022
Observations & remote sensing
New model or method
Classical ML (trees, GP, linear, kernels)NOAA (incl. GFDL, PSL, GSL, NCEP/EMC)University of Oklahoma (AI2ES) Journal
2022-09-06
047
Sep 2022
Shruti Ashok Upadhyaya, Pierre‐Emmanuel Kirstetter, Robert J. Kuligowski et al.
Quarterly Journal of the Royal Meteorological Society
NOAA (incl. GFDL, PSL, GSL, NCEP/EMC), University of Oklahoma (AI2ES)
A Random Forest precipitation estimator using GOES-16 ABI infrared channels and RAP predictors over CONUS beats SCaMPR, with correlation 0.46 and HSS 0.91 vs 0.19.
Towards improved precipitation estimation with the GOES‐16 advanced baseline imager: Algorithm and evaluation
In one sentence. A Random Forest precipitation estimator using GOES-16 ABI infrared channels and RAP predictors over CONUS beats SCaMPR, with correlation 0.46 and HSS 0.91 vs 0.19.
Labels
- Topic
- Observations & remote sensing
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Domain
- Regional
- Data
- Satellite, NWP analysis or forecasts (IFS, GFS, ...), Radar
- Status
- Research
- Identifiers
- DOI:10.1002/qj.4368 OpenAlex:W4294770795
- Added
- 2026-10-22
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