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CITATION.cff
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# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
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: [email protected]
affiliation: European Centre for Medium-Range Weather Forecasts (ECMWF)
- given-names: Ilaria
family-names: Luise
email: [email protected]
affiliation: European Organization for Nuclear Research (CERN)
- given-names: Martin
family-names: Schultz
email: [email protected]
orcid: 'https://orcid.org/0000-0003-3455-774X'
affiliation: Forschungszentrum Jülich (FZJ)
- given-names: Michael
family-names: Langguth
email: [email protected]
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'