paper
2023
Extremes & hazards
Observations & remote sensing New model or method
OtherNASAUniversity of Oklahoma (AI2ES) Journal
2023-06-23
047
Jun 2023
Benjamin R. Scarino, Kyle F. Itterly, Kristopher M. Bedka et al.
Artificial Intelligence for the Earth Systems
NASA, University of Oklahoma (AI2ES)
A deep neural network combining GOES cloud-top satellite data with reanalysis parameters estimates severe hail likelihood, reaching CSI 0.511 against radar MESH.
Deriving Severe Hail Likelihood from Satellite Observations and Model Reanalysis Parameters Using a Deep Neural Network
In one sentence. A deep neural network combining GOES cloud-top satellite data with reanalysis parameters estimates severe hail likelihood, reaching CSI 0.511 against radar MESH.
Labels
- Topic
- Extremes & hazards, Observations & remote sensing
- Type
- New model or method
- Method
- Other
- Domain
- Regional
- Data
- Satellite, ERA5 / reanalysis, Radar
- Status
- Research
- Identifiers
- DOI:10.1175/aies-d-22-0042.1 OpenAlex:W4381738390
- Added
- 2026-10-22
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