paper
2021
Global medium-range NWP
Data assimilation Position or perspective paper
Other3–15 daysForschungszentrum Jülich / Jülich Supercomputing Centre Journal
2021-02-15
042
Feb 2021
M. G. Schultz, C. Betancourt, B. Gong et al.
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Forschungszentrum Jülich / Jülich Supercomputing Centre
Discusses whether deep learning could fully replace numerical weather models and data assimilation, reviewing ML concepts and their fit for weather data.
Can deep learning beat numerical weather prediction?
In one sentence. Discusses whether deep learning could fully replace numerical weather models and data assimilation, reviewing ML concepts and their fit for weather data.
Labels
- Topic
- Global medium-range NWP, Data assimilation
- Type
- Position or perspective paper
- Method
- Other
- Time scale
- 3–15 days
- Domain
- Global
- Data
- NWP analysis or forecasts (IFS, GFS, ...)
- Status
- Research
- Identifiers
- arXiv:2020.0097 DOI:10.1098/rsta.2020.0097 OpenAlex:W3132280960
- Added
- 2026-10-22
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