paper
2021
Nowcasting
Observations & remote sensingExtremes & hazards New model or method
Classical ML (trees, GP, linear, kernels)0–6 hNASAColorado State University (incl. CIRA)University of Oklahoma (AI2ES) Journal
2021-03-08
047
Mar 2021
John R. Mecikalski, Thea N. Sandmæl, Elisa M. Murillo et al.
Monthly Weather Review
NASA, Colorado State University (incl. CIRA), University of Oklahoma (AI2ES)
Random forests combining GOES-16 satellite, lightning and radar fields improve severe storm warning prediction over radar alone, identifying cloud-top divergence as most informative.
Random Forest Model to Assess Predictor Importance and Nowcast Severe Storms using High-Resolution Radar–GOES Satellite–Lightning Observations
In one sentence. Random forests combining GOES-16 satellite, lightning and radar fields improve severe storm warning prediction over radar alone, identifying cloud-top divergence as most informative.
Labels
- Topic
- Nowcasting, Observations & remote sensing, Extremes & hazards
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 0–6 h
- Domain
- Regional
- Data
- Satellite, Radar
- Status
- Research
- Identifiers
- DOI:10.1175/mwr-d-19-0274.1 OpenAlex:W3135212186
- Added
- 2026-10-22
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