ROMS-JEDI Intro - Revision history

Arango: /* Introduction */

2023-09-11T20:21:45Z

Introduction

← Older revision		Revision as of 20:21, 11 September 2023
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	==Introduction==		==Introduction==
	The Joint Effort for Data Assimilation Integration ('''JEDI''') framework is a unified, high-level, state-of-the-art data assimilation ('''DA''') system for scientific exploration and operational forecasting. The diagram above shows that the '''JEDI''' building blocks are model agnostic, and any modern geophysical numerical kernels can be interfaced. Its separable generic design and abstraction allow the implementation of various '''DA''' methodologies, including variational, ensemble, hybrid, and coupled approaches that are common to any geophysical system, without the need to develop such algorithms separately for each model ~~technically~~. The '''JEDI''' framework is centered around the Object-Oriented Prediction System ('''OOPS''') layer classes and operators implemented in '''C++''' using templates. The specific implementations like '''ROMS-JEDI''' are written in Fortran 2003 because of the need for class objects, polymorphism, inheritance, and interoperability between '''C++''' and '''Fortran'''. The interoperability binding allows '''Fortran''' to invoke '''C++''' functions and vice versa '''C++''' to invoke '''Fortran''' procedures.		The Joint Effort for Data Assimilation Integration ('''JEDI''') framework is a unified, high-level, state-of-the-art data assimilation ('''DA''') system for scientific exploration and operational forecasting. The diagram above shows that the '''JEDI''' building blocks are model agnostic, and any modern geophysical numerical kernels can be interfaced. Its separable generic design and abstraction allow the implementation of various '''DA''' methodologies, including variational, ensemble, hybrid, and coupled approaches that are common to any geophysical system, without the need to develop such algorithms separately for each model. The '''JEDI''' framework is centered around the Object-Oriented Prediction System ('''OOPS''') layer classes and operators implemented in '''C++''' using templates. The specific implementations like '''ROMS-JEDI''' are written in Fortran 2003 because of the need for class objects, polymorphism, inheritance, and interoperability between '''C++''' and '''Fortran'''. The interoperability binding allows '''Fortran''' to invoke '''C++''' functions and vice versa '''C++''' to invoke '''Fortran''' procedures.

	The '''JEDI''' separation of concerns design allows the incorporation of diverse (current and future) observational data streams essential for any DA system. Observation operators can be efficiently coded and added to the Unified Forward Operator ('''UFO''') interface, which simulates, interpolates, and relates observable data to the model component state vector, '''H(x)'''. The '''UFO''' interface also implements quality control, data thinning, and bias correction. The Interface for Observation Data Access ('''IODA''') storage layer ingests observational data from providers into the '''UFO''' and '''OOPS''' building blocks. The immense amount of incoming data can be trimmed down, quality-controlled, and filtered by '''UFO''' operators before it is assimilated and during postprocessing when computing diagnostics, impacts, and sensitivities.		The '''JEDI''' separation of concerns design allows the incorporation of diverse (current and future) observational data streams essential for any DA system. Observation operators can be efficiently coded and added to the Unified Forward Operator ('''UFO''') interface, which simulates, interpolates, and relates observable data to the model component state vector, '''H(x)'''. The '''UFO''' interface also implements quality control, data thinning, and bias correction. The Interface for Observation Data Access ('''IODA''') storage layer ingests observational data from providers into the '''UFO''' and '''OOPS''' building blocks. The immense amount of incoming data can be trimmed down, quality-controlled, and filtered by '''UFO''' operators before it is assimilated and during postprocessing when computing diagnostics, impacts, and sensitivities.

	The '''ROMS-JEDI''' framework is intended for users interested in ocean prediction and data assimilation. Currently, we use the '''JEDI''' framework to incorporate physical observational data into regional '''ROMS''' applications using variational ('''3DVar'''/'''4DVar''', '''3D-FGAT'''/'''4D-FGAT'''), ensemble ('''EDA''', '''EnKF''', '''LETKF'''), and hybrid ('''3DEnVar'''/'''4DEnVar''') data assimilation methodologies. The '''ROMS''' Tangent Linear Model ('''TLM''') and Adjoint Model ('''ADM''') kernels are used in the '''4DVar''' family of algorithms.		The '''ROMS-JEDI''' framework is intended for users interested in ocean prediction and data assimilation. Currently, we use the '''JEDI''' framework to incorporate physical observational data into regional '''ROMS''' applications using variational ('''3DVar'''/'''4DVar''', '''3D-FGAT'''/'''4D-FGAT'''), ensemble ('''EDA''', '''EnKF''', '''LETKF'''), and hybrid ('''3DEnVar'''/'''4DEnVar''') data assimilation methodologies. The '''ROMS''' Tangent Linear Model ('''TLM''') and Adjoint Model ('''ADM''') kernels are used in the '''4DVar''' family of algorithms.

Arango at 04:56, 31 August 2023

2023-08-31T04:56:47Z

← Older revision		Revision as of 04:56, 31 August 2023
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	The '''ROMS-JEDI''' framework is intended for users interested in ocean prediction and data assimilation. Currently, we use the '''JEDI''' framework to incorporate physical observational data into regional '''ROMS''' applications using variational ('''3DVar'''/'''4DVar''', '''3D-FGAT'''/'''4D-FGAT'''), ensemble ('''EDA''', '''EnKF''', '''LETKF'''), and hybrid ('''3DEnVar'''/'''4DEnVar''') data assimilation methodologies. The '''ROMS''' Tangent Linear Model ('''TLM''') and Adjoint Model ('''ADM''') kernels are used in the '''4DVar''' family of algorithms.		The '''ROMS-JEDI''' framework is intended for users interested in ocean prediction and data assimilation. Currently, we use the '''JEDI''' framework to incorporate physical observational data into regional '''ROMS''' applications using variational ('''3DVar'''/'''4DVar''', '''3D-FGAT'''/'''4D-FGAT'''), ensemble ('''EDA''', '''EnKF''', '''LETKF'''), and hybrid ('''3DEnVar'''/'''4DEnVar''') data assimilation methodologies. The '''ROMS''' Tangent Linear Model ('''TLM''') and Adjoint Model ('''ADM''') kernels are used in the '''4DVar''' family of algorithms.


	~~<center>[[Image:ROMS-JEDI_circle_logo.png]]   [[Image:ROMS_circle_logo.png]]</center>~~

Robertson at 20:41, 30 August 2023

2023-08-30T20:41:18Z

← Older revision		Revision as of 20:41, 30 August 2023
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	The '''ROMS-JEDI''' framework is intended for users interested in ocean prediction and data assimilation. Currently, we use the '''JEDI''' framework to incorporate physical observational data into regional '''ROMS''' applications using variational ('''3DVar'''/'''4DVar''', '''3D-FGAT'''/'''4D-FGAT'''), ensemble ('''EDA''', '''EnKF''', '''LETKF'''), and hybrid ('''3DEnVar'''/'''4DEnVar''') data assimilation methodologies. The '''ROMS''' Tangent Linear Model ('''TLM''') and Adjoint Model ('''ADM''') kernels are used in the '''4DVar''' family of algorithms.		The '''ROMS-JEDI''' framework is intended for users interested in ocean prediction and data assimilation. Currently, we use the '''JEDI''' framework to incorporate physical observational data into regional '''ROMS''' applications using variational ('''3DVar'''/'''4DVar''', '''3D-FGAT'''/'''4D-FGAT'''), ensemble ('''EDA''', '''EnKF''', '''LETKF'''), and hybrid ('''3DEnVar'''/'''4DEnVar''') data assimilation methodologies. The '''ROMS''' Tangent Linear Model ('''TLM''') and Adjoint Model ('''ADM''') kernels are used in the '''4DVar''' family of algorithms.


			<center>[[Image:ROMS-JEDI_circle_logo.png]]   [[Image:ROMS_circle_logo.png]]</center>

Robertson at 20:24, 30 August 2023

2023-08-30T20:24:40Z

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	<div class="title">ROMS-JEDI</div>		<div class="title">ROMS-JEDI</div>

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Robertson at 20:18, 30 August 2023

2023-08-30T20:18:31Z

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2023-08-30T16:33:31Z

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==Introduction==
The Joint Effort for Data Assimilation Integration ('''JEDI''') framework is a unified, high-level, state-of-the-art data assimilation ('''DA''') system for scientific exploration and operational forecasting. The diagram above shows that the '''JEDI''' building blocks are model agnostic, and any modern geophysical numerical kernels can be interfaced. Its separable generic design and abstraction allow the implementation of various '''DA''' methodologies, including variational, ensemble, hybrid, and coupled approaches that are common to any geophysical system, without the need to develop such algorithms separately for each model technically. The '''JEDI''' framework is centered around the Object-Oriented Prediction System ('''OOPS''') layer classes and operators implemented in '''C++''' using templates. The specific implementations like '''ROMS-JEDI''' are written in Fortran 2003 because of the need for class objects, polymorphism, inheritance, and interoperability between '''C++''' and '''Fortran'''. The interoperability binding allows '''Fortran''' to invoke '''C++''' functions and vice versa '''C++''' to invoke '''Fortran''' procedures.

The '''JEDI''' separation of concerns design allows the incorporation of diverse (current and future) observational data streams essential for any DA system. Observation operators can be efficiently coded and added to the Unified Forward Operator ('''UFO''') interface, which simulates, interpolates, and relates observable data to the model component state vector, '''H(x)'''. The '''UFO''' interface also implements quality control, data thinning, and bias correction. The Interface for Observation Data Access ('''IODA''') storage layer ingests observational data from providers into the '''UFO''' and '''OOPS''' building blocks. The immense amount of incoming data can be trimmed down, quality-controlled, and filtered by '''UFO''' operators before it is assimilated and during postprocessing when computing diagnostics, impacts, and sensitivities.

The '''ROMS-JEDI''' framework is intended for users interested in ocean prediction and data assimilation. Currently, we use the '''JEDI''' framework to incorporate physical observational data into regional '''ROMS''' applications using variational ('''3DVar'''/'''4DVar''', '''3D-FGAT'''/'''4D-FGAT'''), ensemble ('''EDA''', '''EnKF''', '''LETKF'''), and hybrid ('''3DEnVar'''/'''4DEnVar''') data assimilation methodologies. The '''ROMS''' Tangent Linear Model ('''TLM''') and Adjoint Model ('''ADM''') kernels are used in the '''4DVar''' family of algorithms.