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  Physically constrained generative adversarial networks for improving precipitation fields from Earth system models

Hess, P., Drüke, M., Petri, S., Strnad, F., Boers, N. (2022 online): Physically constrained generative adversarial networks for improving precipitation fields from Earth system models. - Nature Machine Intelligence.
https://doi.org/10.1038/s42256-022-00540-1

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Hess, Philipp1, Autor              
Drüke, Markus1, Autor              
Petri, Stefan1, Autor              
Strnad, Felix1, Autor              
Boers, Niklas1, Autor              
Affiliations:
1Potsdam Institute for Climate Impact Research, Potsdam, ou_persistent13              

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 Zusammenfassung: Precipitation results from complex processes across many scales, making its accurate simulation in Earth system models (ESMs) challenging. Existing post-processing methods can improve ESM simulations locally but cannot correct errors in modelled spatial patterns. Here we propose a framework based on physically constrained generative adversarial networks to improve local distributions and spatial structure simultaneously. We apply our approach to the computationally efficient CM2Mc–LPJmL ESM. Our method outperforms existing ones in correcting local distributions and leads to strongly improved spatial patterns, especially regarding the intermittency of daily precipitation. Notably, a double-peaked Intertropical Convergence Zone, a common problem in ESMs, is removed. Enforcing a physical constraint to preserve global precipitation sums, the generative adversarial network can generalize to future climate scenarios unseen during training. Feature attribution shows that the generative adversarial network identifies regions where the ESM exhibits strong biases. Our method constitutes a general framework for correcting ESM variables and enables realistic simulations at a fraction of the computational cost.

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Sprache(n): eng - Englisch
 Datum: 2022-09-012022-10-03
 Publikationsstatus: Online veröffentlicht
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1038/s42256-022-00540-1
PIKDOMAIN: RD4 - Complexity Science
PIKDOMAIN: RD1 - Earth System Analysis
Research topic keyword: Atmosphere
Regional keyword: Global
Model / method: Machine Learning
Model / method: POEM
Organisational keyword: FutureLab - Artificial Intelligence in the Anthropocene
MDB-ID: No MDB - stored outside PIK (see DOI)
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Titel: Nature Machine Intelligence
Genre der Quelle: Zeitschrift, SCI, Scopus
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Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: - Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/nature-machine-intelligence
Publisher: Springer Nature