A Hybrid Approach to Atmospheric Modeling That Combines Machine Learning With a Physics‐Based Numerical Model.
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| Title: | A Hybrid Approach to Atmospheric Modeling That Combines Machine Learning With a Physics‐Based Numerical Model. |
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| Authors: | Arcomano, Troy1 (AUTHOR) troyarcomano@tamu.edu, Szunyogh, Istvan1 (AUTHOR), Wikner, Alexander2 (AUTHOR), Pathak, Jaideep3 (AUTHOR), Hunt, Brian R.4 (AUTHOR), Ott, Edward2,5 (AUTHOR) |
| Source: | Journal of Advances in Modeling Earth Systems. Mar2022, Vol. 14 Issue 3, p1-21. 21p. |
| Subject Terms: | *General circulation model, *Climate research, *Humidity, Atmospheric models, Machine learning, Surface of the earth |
| Abstract: | This paper describes an implementation of the combined hybrid‐parallel prediction (CHyPP) approach of Wikner et al. (2020), https://doi.org/10.1063/5.0005541 on a low‐resolution atmospheric global circulation model (AGCM). The CHyPP approach combines a physics‐based numerical model of a dynamical system (e.g., the atmosphere) with a computationally efficient type of machine learning (ML) called reservoir computing to construct a hybrid model. This hybrid atmospheric model produces more accurate forecasts of most atmospheric state variables than the host AGCM for the first 7–8 forecast days, and for even longer times for the temperature and humidity near the earth's surface. It also produces more accurate forecasts than a model based only on ML, or a model that combines linear regression, rather than ML, with the AGCM. The potential of the CHyPP approach for climate research is demonstrated by a 10‐year long hybrid model simulation of the atmospheric general circulation, which shows that the hybrid model can simulate the general circulation with substantially smaller systematic errors and more realistic variability than the host AGCM. Plain Language Summary: This paper presents a computationally efficient novel approach to construct a hybrid model of the atmosphere by combining a physics‐based model of the global atmospheric circulation with a machine learning component. The primary purpose of the hybrid model is to produce quantitative weather forecasts on the same grid as the physics‐based model. It is found that the hybrid model produces more accurate forecasts than the host physics‐based model for the first 7–8 forecast days for most forecast variables, and for even longer times for the temperature and humidity near the Earth's surface. Furthermore, the hybrid model is found to simulate the climate with substantially smaller systematic errors and more realistic temporal variability than the host model. Key Points: A hybrid model incorporating machine learning produces more accurate forecasts and more realistic climate than the host physics‐based modelThe hybrid model states are more realistically balanced and have substantially lower biases than the host modelThe hybrid model produces more realistic atmospheric variability than the host model at time scales shorter than about a week [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Advances in Modeling Earth Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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| Header | DbId: 8gh DbLabel: GreenFILE An: 155977523 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Hybrid Approach to Atmospheric Modeling That Combines Machine Learning With a Physics‐Based Numerical Model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Arcomano%2C+Troy%22">Arcomano, Troy</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> troyarcomano@tamu.edu</i><br /><searchLink fieldCode="AR" term="%22Szunyogh%2C+Istvan%22">Szunyogh, Istvan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wikner%2C+Alexander%22">Wikner, Alexander</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pathak%2C+Jaideep%22">Pathak, Jaideep</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hunt%2C+Brian+R%2E%22">Hunt, Brian R.</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ott%2C+Edward%22">Ott, Edward</searchLink><relatesTo>2,5</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Advances+in+Modeling+Earth+Systems%22">Journal of Advances in Modeling Earth Systems</searchLink>. Mar2022, Vol. 14 Issue 3, p1-21. 21p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22General+circulation+model%22">General circulation model</searchLink><br />*<searchLink fieldCode="DE" term="%22Climate+research%22">Climate research</searchLink><br />*<searchLink fieldCode="DE" term="%22Humidity%22">Humidity</searchLink><br /><searchLink fieldCode="DE" term="%22Atmospheric+models%22">Atmospheric models</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Surface+of+the+earth%22">Surface of the earth</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper describes an implementation of the combined hybrid‐parallel prediction (CHyPP) approach of Wikner et al. (2020), https://doi.org/10.1063/5.0005541 on a low‐resolution atmospheric global circulation model (AGCM). The CHyPP approach combines a physics‐based numerical model of a dynamical system (e.g., the atmosphere) with a computationally efficient type of machine learning (ML) called reservoir computing to construct a hybrid model. This hybrid atmospheric model produces more accurate forecasts of most atmospheric state variables than the host AGCM for the first 7–8 forecast days, and for even longer times for the temperature and humidity near the earth's surface. It also produces more accurate forecasts than a model based only on ML, or a model that combines linear regression, rather than ML, with the AGCM. The potential of the CHyPP approach for climate research is demonstrated by a 10‐year long hybrid model simulation of the atmospheric general circulation, which shows that the hybrid model can simulate the general circulation with substantially smaller systematic errors and more realistic variability than the host AGCM. Plain Language Summary: This paper presents a computationally efficient novel approach to construct a hybrid model of the atmosphere by combining a physics‐based model of the global atmospheric circulation with a machine learning component. The primary purpose of the hybrid model is to produce quantitative weather forecasts on the same grid as the physics‐based model. It is found that the hybrid model produces more accurate forecasts than the host physics‐based model for the first 7–8 forecast days for most forecast variables, and for even longer times for the temperature and humidity near the Earth's surface. Furthermore, the hybrid model is found to simulate the climate with substantially smaller systematic errors and more realistic temporal variability than the host model. Key Points: A hybrid model incorporating machine learning produces more accurate forecasts and more realistic climate than the host physics‐based modelThe hybrid model states are more realistically balanced and have substantially lower biases than the host modelThe hybrid model produces more realistic atmospheric variability than the host model at time scales shorter than about a week [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Advances in Modeling Earth Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1029/2021MS002712 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 1 Subjects: – SubjectFull: General circulation model Type: general – SubjectFull: Climate research Type: general – SubjectFull: Humidity Type: general – SubjectFull: Atmospheric models Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Surface of the earth Type: general Titles: – TitleFull: A Hybrid Approach to Atmospheric Modeling That Combines Machine Learning With a Physics‐Based Numerical Model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Arcomano, Troy – PersonEntity: Name: NameFull: Szunyogh, Istvan – PersonEntity: Name: NameFull: Wikner, Alexander – PersonEntity: Name: NameFull: Pathak, Jaideep – PersonEntity: Name: NameFull: Hunt, Brian R. – PersonEntity: Name: NameFull: Ott, Edward IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 19422466 Numbering: – Type: volume Value: 14 – Type: issue Value: 3 Titles: – TitleFull: Journal of Advances in Modeling Earth Systems Type: main |
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