An effective parameter optimization with radiation balance constraint in CAM5 (version 5.3)

<p<Uncertain parameters in physical parameterizations of general circulation models (GCMs) greatly impact model performance. In recent years, automatic parameter optimization has been introduced for tuning model performance of GCMs, but most of the optimization methods are unconstrained optimi...
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Autor*in:	L. Wu [verfasserIn] T. Zhang [verfasserIn] Y. Qin [verfasserIn] W. Xue [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Übergeordnetes Werk:	In: Geoscientific Model Development - Copernicus Publications, 2009, 13(2020), Seite 41-53
Übergeordnetes Werk:	volume:13 ; year:2020 ; pages:41-53

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.5194/gmd-13-41-2020

Katalog-ID:	DOAJ068457855

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spelling	10.5194/gmd-13-41-2020 doi (DE-627)DOAJ068457855 (DE-599)DOAJ1bafd660d67c4a959915e67743c1568c DE-627 ger DE-627 rakwb eng QE1-996.5 L. Wu verfasserin aut An effective parameter optimization with radiation balance constraint in CAM5 (version 5.3) 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Uncertain parameters in physical parameterizations of general circulation models (GCMs) greatly impact model performance. In recent years, automatic parameter optimization has been introduced for tuning model performance of GCMs, but most of the optimization methods are unconstrained optimization methods under a given performance indicator. Therefore, the calibrated model may break through essential constraints that models have to keep, such as the radiation balance at the top of the model. The radiation balance is known for its importance in the conservation of model energy. In this study, an automated and efficient parameter optimization with the radiation balance constraint is presented and applied in the Community Atmospheric Model (CAM5) in terms of a synthesized performance metric using normalized mean square error of radiation, precipitation, relative humidity, and temperature. The tuned parameters are from the parameterization schemes of convection and cloud. The radiation constraint is defined as the absolute difference of the net longwave flux at the top of the model (FLNT) and the net solar flux at the top of the model (FSNT) of less than 1 W m<span class="inline-formula"<<sup<−2</sup<</span<. Results show that the synthesized performance under the optimal parameters is 6.3 % better than the control run (CNTL) and the radiation imbalance is as low as 0.1 W m<span class="inline-formula"<<sup<−2</sup<</span<. The proposed method provides an insight for physics-guided optimization, and it can be easily applied to optimization problems with other prerequisite constraints in GCMs.</p< Geology T. Zhang verfasserin aut Y. Qin verfasserin aut Y. Qin verfasserin aut W. Xue verfasserin aut W. Xue verfasserin aut W. Xue verfasserin aut In Geoscientific Model Development Copernicus Publications, 2009 13(2020), Seite 41-53 (DE-627)582024102 (DE-600)2456725-5 19919603 nnns volume:13 year:2020 pages:41-53 https://doi.org/10.5194/gmd-13-41-2020 kostenfrei https://doaj.org/article/1bafd660d67c4a959915e67743c1568c kostenfrei https://www.geosci-model-dev.net/13/41/2020/gmd-13-41-2020.pdf kostenfrei https://doaj.org/toc/1991-959X Journal toc kostenfrei https://doaj.org/toc/1991-9603 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2020 41-53
allfields_unstemmed	10.5194/gmd-13-41-2020 doi (DE-627)DOAJ068457855 (DE-599)DOAJ1bafd660d67c4a959915e67743c1568c DE-627 ger DE-627 rakwb eng QE1-996.5 L. Wu verfasserin aut An effective parameter optimization with radiation balance constraint in CAM5 (version 5.3) 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Uncertain parameters in physical parameterizations of general circulation models (GCMs) greatly impact model performance. In recent years, automatic parameter optimization has been introduced for tuning model performance of GCMs, but most of the optimization methods are unconstrained optimization methods under a given performance indicator. Therefore, the calibrated model may break through essential constraints that models have to keep, such as the radiation balance at the top of the model. The radiation balance is known for its importance in the conservation of model energy. In this study, an automated and efficient parameter optimization with the radiation balance constraint is presented and applied in the Community Atmospheric Model (CAM5) in terms of a synthesized performance metric using normalized mean square error of radiation, precipitation, relative humidity, and temperature. The tuned parameters are from the parameterization schemes of convection and cloud. The radiation constraint is defined as the absolute difference of the net longwave flux at the top of the model (FLNT) and the net solar flux at the top of the model (FSNT) of less than 1 W m<span class="inline-formula"<<sup<−2</sup<</span<. Results show that the synthesized performance under the optimal parameters is 6.3 % better than the control run (CNTL) and the radiation imbalance is as low as 0.1 W m<span class="inline-formula"<<sup<−2</sup<</span<. The proposed method provides an insight for physics-guided optimization, and it can be easily applied to optimization problems with other prerequisite constraints in GCMs.</p< Geology T. Zhang verfasserin aut Y. Qin verfasserin aut Y. Qin verfasserin aut W. Xue verfasserin aut W. Xue verfasserin aut W. Xue verfasserin aut In Geoscientific Model Development Copernicus Publications, 2009 13(2020), Seite 41-53 (DE-627)582024102 (DE-600)2456725-5 19919603 nnns volume:13 year:2020 pages:41-53 https://doi.org/10.5194/gmd-13-41-2020 kostenfrei https://doaj.org/article/1bafd660d67c4a959915e67743c1568c kostenfrei https://www.geosci-model-dev.net/13/41/2020/gmd-13-41-2020.pdf kostenfrei https://doaj.org/toc/1991-959X Journal toc kostenfrei https://doaj.org/toc/1991-9603 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2020 41-53
allfieldsGer	10.5194/gmd-13-41-2020 doi (DE-627)DOAJ068457855 (DE-599)DOAJ1bafd660d67c4a959915e67743c1568c DE-627 ger DE-627 rakwb eng QE1-996.5 L. Wu verfasserin aut An effective parameter optimization with radiation balance constraint in CAM5 (version 5.3) 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Uncertain parameters in physical parameterizations of general circulation models (GCMs) greatly impact model performance. In recent years, automatic parameter optimization has been introduced for tuning model performance of GCMs, but most of the optimization methods are unconstrained optimization methods under a given performance indicator. Therefore, the calibrated model may break through essential constraints that models have to keep, such as the radiation balance at the top of the model. The radiation balance is known for its importance in the conservation of model energy. In this study, an automated and efficient parameter optimization with the radiation balance constraint is presented and applied in the Community Atmospheric Model (CAM5) in terms of a synthesized performance metric using normalized mean square error of radiation, precipitation, relative humidity, and temperature. The tuned parameters are from the parameterization schemes of convection and cloud. The radiation constraint is defined as the absolute difference of the net longwave flux at the top of the model (FLNT) and the net solar flux at the top of the model (FSNT) of less than 1 W m<span class="inline-formula"<<sup<−2</sup<</span<. Results show that the synthesized performance under the optimal parameters is 6.3 % better than the control run (CNTL) and the radiation imbalance is as low as 0.1 W m<span class="inline-formula"<<sup<−2</sup<</span<. The proposed method provides an insight for physics-guided optimization, and it can be easily applied to optimization problems with other prerequisite constraints in GCMs.</p< Geology T. Zhang verfasserin aut Y. Qin verfasserin aut Y. Qin verfasserin aut W. Xue verfasserin aut W. Xue verfasserin aut W. Xue verfasserin aut In Geoscientific Model Development Copernicus Publications, 2009 13(2020), Seite 41-53 (DE-627)582024102 (DE-600)2456725-5 19919603 nnns volume:13 year:2020 pages:41-53 https://doi.org/10.5194/gmd-13-41-2020 kostenfrei https://doaj.org/article/1bafd660d67c4a959915e67743c1568c kostenfrei https://www.geosci-model-dev.net/13/41/2020/gmd-13-41-2020.pdf kostenfrei https://doaj.org/toc/1991-959X Journal toc kostenfrei https://doaj.org/toc/1991-9603 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2020 41-53
allfieldsSound	10.5194/gmd-13-41-2020 doi (DE-627)DOAJ068457855 (DE-599)DOAJ1bafd660d67c4a959915e67743c1568c DE-627 ger DE-627 rakwb eng QE1-996.5 L. Wu verfasserin aut An effective parameter optimization with radiation balance constraint in CAM5 (version 5.3) 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Uncertain parameters in physical parameterizations of general circulation models (GCMs) greatly impact model performance. In recent years, automatic parameter optimization has been introduced for tuning model performance of GCMs, but most of the optimization methods are unconstrained optimization methods under a given performance indicator. Therefore, the calibrated model may break through essential constraints that models have to keep, such as the radiation balance at the top of the model. The radiation balance is known for its importance in the conservation of model energy. In this study, an automated and efficient parameter optimization with the radiation balance constraint is presented and applied in the Community Atmospheric Model (CAM5) in terms of a synthesized performance metric using normalized mean square error of radiation, precipitation, relative humidity, and temperature. The tuned parameters are from the parameterization schemes of convection and cloud. The radiation constraint is defined as the absolute difference of the net longwave flux at the top of the model (FLNT) and the net solar flux at the top of the model (FSNT) of less than 1 W m<span class="inline-formula"<<sup<−2</sup<</span<. Results show that the synthesized performance under the optimal parameters is 6.3 % better than the control run (CNTL) and the radiation imbalance is as low as 0.1 W m<span class="inline-formula"<<sup<−2</sup<</span<. The proposed method provides an insight for physics-guided optimization, and it can be easily applied to optimization problems with other prerequisite constraints in GCMs.</p< Geology T. Zhang verfasserin aut Y. Qin verfasserin aut Y. Qin verfasserin aut W. Xue verfasserin aut W. Xue verfasserin aut W. Xue verfasserin aut In Geoscientific Model Development Copernicus Publications, 2009 13(2020), Seite 41-53 (DE-627)582024102 (DE-600)2456725-5 19919603 nnns volume:13 year:2020 pages:41-53 https://doi.org/10.5194/gmd-13-41-2020 kostenfrei https://doaj.org/article/1bafd660d67c4a959915e67743c1568c kostenfrei https://www.geosci-model-dev.net/13/41/2020/gmd-13-41-2020.pdf kostenfrei https://doaj.org/toc/1991-959X Journal toc kostenfrei https://doaj.org/toc/1991-9603 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2020 41-53
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callnumber-first	Q - Science
author	L. Wu
spellingShingle	L. Wu misc QE1-996.5 misc Geology An effective parameter optimization with radiation balance constraint in CAM5 (version 5.3)
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topic_title	QE1-996.5 An effective parameter optimization with radiation balance constraint in CAM5 (version 5.3)
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title	An effective parameter optimization with radiation balance constraint in CAM5 (version 5.3)
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title_sort	effective parameter optimization with radiation balance constraint in cam5 (version 5.3)
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title_auth	An effective parameter optimization with radiation balance constraint in CAM5 (version 5.3)
abstract	<p<Uncertain parameters in physical parameterizations of general circulation models (GCMs) greatly impact model performance. In recent years, automatic parameter optimization has been introduced for tuning model performance of GCMs, but most of the optimization methods are unconstrained optimization methods under a given performance indicator. Therefore, the calibrated model may break through essential constraints that models have to keep, such as the radiation balance at the top of the model. The radiation balance is known for its importance in the conservation of model energy. In this study, an automated and efficient parameter optimization with the radiation balance constraint is presented and applied in the Community Atmospheric Model (CAM5) in terms of a synthesized performance metric using normalized mean square error of radiation, precipitation, relative humidity, and temperature. The tuned parameters are from the parameterization schemes of convection and cloud. The radiation constraint is defined as the absolute difference of the net longwave flux at the top of the model (FLNT) and the net solar flux at the top of the model (FSNT) of less than 1 W m<span class="inline-formula"<<sup<−2</sup<</span<. Results show that the synthesized performance under the optimal parameters is 6.3 % better than the control run (CNTL) and the radiation imbalance is as low as 0.1 W m<span class="inline-formula"<<sup<−2</sup<</span<. The proposed method provides an insight for physics-guided optimization, and it can be easily applied to optimization problems with other prerequisite constraints in GCMs.</p<
abstractGer	<p<Uncertain parameters in physical parameterizations of general circulation models (GCMs) greatly impact model performance. In recent years, automatic parameter optimization has been introduced for tuning model performance of GCMs, but most of the optimization methods are unconstrained optimization methods under a given performance indicator. Therefore, the calibrated model may break through essential constraints that models have to keep, such as the radiation balance at the top of the model. The radiation balance is known for its importance in the conservation of model energy. In this study, an automated and efficient parameter optimization with the radiation balance constraint is presented and applied in the Community Atmospheric Model (CAM5) in terms of a synthesized performance metric using normalized mean square error of radiation, precipitation, relative humidity, and temperature. The tuned parameters are from the parameterization schemes of convection and cloud. The radiation constraint is defined as the absolute difference of the net longwave flux at the top of the model (FLNT) and the net solar flux at the top of the model (FSNT) of less than 1 W m<span class="inline-formula"<<sup<−2</sup<</span<. Results show that the synthesized performance under the optimal parameters is 6.3 % better than the control run (CNTL) and the radiation imbalance is as low as 0.1 W m<span class="inline-formula"<<sup<−2</sup<</span<. The proposed method provides an insight for physics-guided optimization, and it can be easily applied to optimization problems with other prerequisite constraints in GCMs.</p<
abstract_unstemmed	<p<Uncertain parameters in physical parameterizations of general circulation models (GCMs) greatly impact model performance. In recent years, automatic parameter optimization has been introduced for tuning model performance of GCMs, but most of the optimization methods are unconstrained optimization methods under a given performance indicator. Therefore, the calibrated model may break through essential constraints that models have to keep, such as the radiation balance at the top of the model. The radiation balance is known for its importance in the conservation of model energy. In this study, an automated and efficient parameter optimization with the radiation balance constraint is presented and applied in the Community Atmospheric Model (CAM5) in terms of a synthesized performance metric using normalized mean square error of radiation, precipitation, relative humidity, and temperature. The tuned parameters are from the parameterization schemes of convection and cloud. The radiation constraint is defined as the absolute difference of the net longwave flux at the top of the model (FLNT) and the net solar flux at the top of the model (FSNT) of less than 1 W m<span class="inline-formula"<<sup<−2</sup<</span<. Results show that the synthesized performance under the optimal parameters is 6.3 % better than the control run (CNTL) and the radiation imbalance is as low as 0.1 W m<span class="inline-formula"<<sup<−2</sup<</span<. The proposed method provides an insight for physics-guided optimization, and it can be easily applied to optimization problems with other prerequisite constraints in GCMs.</p<
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title_short	An effective parameter optimization with radiation balance constraint in CAM5 (version 5.3)
url	https://doi.org/10.5194/gmd-13-41-2020 https://doaj.org/article/1bafd660d67c4a959915e67743c1568c https://www.geosci-model-dev.net/13/41/2020/gmd-13-41-2020.pdf https://doaj.org/toc/1991-959X https://doaj.org/toc/1991-9603
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The radiation constraint is defined as the absolute difference of the net longwave flux at the top of the model (FLNT) and the net solar flux at the top of the model (FSNT) of less than 1 W m<span class="inline-formula"<<sup<−2</sup<</span<. Results show that the synthesized performance under the optimal parameters is 6.3 % better than the control run (CNTL) and the radiation imbalance is as low as 0.1 W m<span class="inline-formula"<<sup<−2</sup<</span<. The proposed method provides an insight for physics-guided optimization, and it can be easily applied to optimization problems with other prerequisite constraints in GCMs.</p<</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Geology</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">T. Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Y. 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An effective parameter optimization with radiation balance constraint in CAM5 (version 5.3)

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