A Fast and Generic Method to Identify Parameters in Complex and Embedded Geophysical Models: The Example of Turbulent Mixing in the Ocean

Abstract Geophysical models make predictions relying on parameter values to be estimated from data. However, existing methods are costly because they require either many runs of the complex geophysical model or to implement an adjoint model. Here, we propose an alternative approach based on optimal...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Clement Aldebert, Guillaume Koenig, Melika Baklouti, Philippe Fraunié, Jean‐Luc Devenon
Formato:	article
Lenguaje:	EN
Publicado:	American Geophysical Union (AGU) 2021
Materias:	parameter identification optimization turbulence hydrodynamics inverse method Physical geography GB3-5030 Oceanography GC1-1581
Acceso en línea:	https://doaj.org/article/981f6a12dd764bdba8b4073d62929f0c
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:981f6a12dd764bdba8b4073d62929f0c
record_format	dspace
spelling	oai:doaj.org-article:981f6a12dd764bdba8b4073d62929f0c2021-11-12T07:13:23ZA Fast and Generic Method to Identify Parameters in Complex and Embedded Geophysical Models: The Example of Turbulent Mixing in the Ocean1942-246610.1029/2020MS002245https://doaj.org/article/981f6a12dd764bdba8b4073d62929f0c2021-08-01T00:00:00Zhttps://doi.org/10.1029/2020MS002245https://doaj.org/toc/1942-2466Abstract Geophysical models make predictions relying on parameter values to be estimated from data. However, existing methods are costly because they require either many runs of the complex geophysical model or to implement an adjoint model. Here, we propose an alternative approach based on optimal control theory which is the simultaneous perturbations stochastic approximation (SPSA). This gradient‐descent method is generic and easy to implement, and its computational cost does not increase with the number of parameters to optimize. This study aims at highlighting the potential of SPSA for parameter identification in geophysical models. Through the example of vertical turbulent mixing in the upper ocean, we show with twin experiments that the method could successfully identify parameter values that minimize model‐data discrepancy. The efficient and easy‐to‐get results provided by SPSA in this study should pave the way for a broader use of parameter identification in the complex and embedded models commonly used in geophysical sciences.Clement AldebertGuillaume KoenigMelika BakloutiPhilippe FrauniéJean‐Luc DevenonAmerican Geophysical Union (AGU)articleparameter identificationoptimizationturbulencehydrodynamicsinverse methodPhysical geographyGB3-5030OceanographyGC1-1581ENJournal of Advances in Modeling Earth Systems, Vol 13, Iss 8, Pp n/a-n/a (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	parameter identification optimization turbulence hydrodynamics inverse method Physical geography GB3-5030 Oceanography GC1-1581
spellingShingle	parameter identification optimization turbulence hydrodynamics inverse method Physical geography GB3-5030 Oceanography GC1-1581 Clement Aldebert Guillaume Koenig Melika Baklouti Philippe Fraunié Jean‐Luc Devenon A Fast and Generic Method to Identify Parameters in Complex and Embedded Geophysical Models: The Example of Turbulent Mixing in the Ocean
description	Abstract Geophysical models make predictions relying on parameter values to be estimated from data. However, existing methods are costly because they require either many runs of the complex geophysical model or to implement an adjoint model. Here, we propose an alternative approach based on optimal control theory which is the simultaneous perturbations stochastic approximation (SPSA). This gradient‐descent method is generic and easy to implement, and its computational cost does not increase with the number of parameters to optimize. This study aims at highlighting the potential of SPSA for parameter identification in geophysical models. Through the example of vertical turbulent mixing in the upper ocean, we show with twin experiments that the method could successfully identify parameter values that minimize model‐data discrepancy. The efficient and easy‐to‐get results provided by SPSA in this study should pave the way for a broader use of parameter identification in the complex and embedded models commonly used in geophysical sciences.
format	article
author	Clement Aldebert Guillaume Koenig Melika Baklouti Philippe Fraunié Jean‐Luc Devenon
author_facet	Clement Aldebert Guillaume Koenig Melika Baklouti Philippe Fraunié Jean‐Luc Devenon
author_sort	Clement Aldebert
title	A Fast and Generic Method to Identify Parameters in Complex and Embedded Geophysical Models: The Example of Turbulent Mixing in the Ocean
title_short	A Fast and Generic Method to Identify Parameters in Complex and Embedded Geophysical Models: The Example of Turbulent Mixing in the Ocean
title_full	A Fast and Generic Method to Identify Parameters in Complex and Embedded Geophysical Models: The Example of Turbulent Mixing in the Ocean
title_fullStr	A Fast and Generic Method to Identify Parameters in Complex and Embedded Geophysical Models: The Example of Turbulent Mixing in the Ocean
title_full_unstemmed	A Fast and Generic Method to Identify Parameters in Complex and Embedded Geophysical Models: The Example of Turbulent Mixing in the Ocean
title_sort	fast and generic method to identify parameters in complex and embedded geophysical models: the example of turbulent mixing in the ocean
publisher	American Geophysical Union (AGU)
publishDate	2021
url	https://doaj.org/article/981f6a12dd764bdba8b4073d62929f0c
work_keys_str_mv	AT clementaldebert afastandgenericmethodtoidentifyparametersincomplexandembeddedgeophysicalmodelstheexampleofturbulentmixingintheocean AT guillaumekoenig afastandgenericmethodtoidentifyparametersincomplexandembeddedgeophysicalmodelstheexampleofturbulentmixingintheocean AT melikabaklouti afastandgenericmethodtoidentifyparametersincomplexandembeddedgeophysicalmodelstheexampleofturbulentmixingintheocean AT philippefraunie afastandgenericmethodtoidentifyparametersincomplexandembeddedgeophysicalmodelstheexampleofturbulentmixingintheocean AT jeanlucdevenon afastandgenericmethodtoidentifyparametersincomplexandembeddedgeophysicalmodelstheexampleofturbulentmixingintheocean AT clementaldebert fastandgenericmethodtoidentifyparametersincomplexandembeddedgeophysicalmodelstheexampleofturbulentmixingintheocean AT guillaumekoenig fastandgenericmethodtoidentifyparametersincomplexandembeddedgeophysicalmodelstheexampleofturbulentmixingintheocean AT melikabaklouti fastandgenericmethodtoidentifyparametersincomplexandembeddedgeophysicalmodelstheexampleofturbulentmixingintheocean AT philippefraunie fastandgenericmethodtoidentifyparametersincomplexandembeddedgeophysicalmodelstheexampleofturbulentmixingintheocean AT jeanlucdevenon fastandgenericmethodtoidentifyparametersincomplexandembeddedgeophysicalmodelstheexampleofturbulentmixingintheocean
_version_	1718431123066847232

A Fast and Generic Method to Identify Parameters in Complex and Embedded Geophysical Models: The Example of Turbulent Mixing in the Ocean

Ejemplares similares