paper
2022
Benchmarks, datasets & evaluation
Position or perspective paper
ECMWFForschungszentrum Jülich / Jülich Supercomputing CentreNSF NCAR / UCARNVIDIAUniversity of Oklahoma (AI2ES) Journal
2022-07-01
055
Jul 2022
Peter D. Dueben, Martin G. Schultz, Matthew Chantry et al.
Artificial Intelligence for the Earth Systems
ECMWF, Forschungszentrum Jülich / Jülich Supercomputing Centre, NSF NCAR / UCAR, NVIDIA, University of Oklahoma (AI2ES)
Provides a recipe for building atmospheric-science ML benchmark datasets and lists domain-specific challenges and needed benchmarks for weather, climate and air quality.
Benchmarks, datasets & evaluation
Challenges and Benchmark Datasets for Machine Learning in the Atmospheric Sciences: Definition, Status, and Outlook
In one sentence. Provides a recipe for building atmospheric-science ML benchmark datasets and lists domain-specific challenges and needed benchmarks for weather, climate and air quality.
Labels
- Topic
- Benchmarks, datasets & evaluation
- Type
- Position or perspective paper
- Domain
- Global
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.1175/aies-d-21-0002.1 OpenAlex:W4289828962
- Added
- 2026-10-22
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