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| cff-version: 1.2.0 |
| title: Atmorep |
| message: >- |
| If you use this software, please cite it using the |
| metadata from this file. |
| type: software |
| authors: |
| - given-names: Christian |
| family-names: Lessig |
| email: christian.lessig@ecmwf.int |
| affiliation: European Centre for Medium-Range Weather Forecasts (ECMWF) |
| - given-names: Ilaria |
| family-names: Luise |
| email: ilaria.luise@cern.ch |
| affiliation: European Organization for Nuclear Research (CERN) |
| - given-names: Martin |
| family-names: Schultz |
| email: m.schultz@fz-juelich.de |
| orcid: 'https://orcid.org/0000-0003-3455-774X' |
| affiliation: Forschungszentrum Jülich (FZJ) |
| - given-names: Michael |
| family-names: Langguth |
| email: m.langguth@fz-juelich.de |
| orcid: 'https://orcid.org/0000-0003-3354-5333' |
| affiliation: Forschungszentrum Jülich (FZJ) |
| identifiers: |
| - type: url |
| value: 'https://arxiv.org/abs/2308.13280' |
| description: corresponding Preprint |
| repository-code: 'https://isggit.cs.uni-magdeburg.de/atmorep/atmorep' |
| url: 'https://www.atmorep.org' |
| abstract: >- |
| AtmoRep is a novel, task-independent stochastic computer |
| model of atmospheric dynamics that can provide skillful |
| results for a wide range of applications. AtmoRep uses |
| large-scale representation learning from artificial |
| intelligence to determine a general description of the |
| highly complex, stochastic dynamics of the atmosphere |
| from the best available estimate of the system's historical |
| trajectory as constrained by observations. This is enabled |
| by a novel self-supervised learning objective and a unique |
| ensemble that samples from the stochastic model with a |
| variability informed by the one in the historical record. |
| Our work establishes that large-scale neural networks can |
| provide skillful, task-independent models of atmospheric |
| dynamics. With this, they provide a novel means to make |
| the large record of atmospheric observations accessible |
| for applications and for scientific inquiry, complementing |
| existing simulations based on first principles. |
| license: MIT |
| commit: b0da5b32ec70295914bbb486dbcb77885671dc45 |
| version: 2.0 (preprint) |
| date-released: '2023-11-28' |
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