Data assimilation in a sparsely observed one-dimensional modeled MHD system

A one dimensional non-linear magneto-hydrodynamic (MHD) system has been introduced to test a sequential optimal interpolation assimilation technique that uses a Monte-Carlo method to calculate the forecast error covariance. An ensemble of 100 model runs with perturbed initial conditions are used to...

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Main Authors: Z. Sun, A. Tangborn, W. Kuang
Format: Article
Language:English
Published: Copernicus Publications 2007-01-01
Series:Nonlinear Processes in Geophysics
Online Access:http://www.nonlin-processes-geophys.net/14/181/2007/npg-14-181-2007.pdf
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author Z. Sun
A. Tangborn
A. Tangborn
W. Kuang
author_facet Z. Sun
A. Tangborn
A. Tangborn
W. Kuang
author_sort Z. Sun
collection DOAJ
description A one dimensional non-linear magneto-hydrodynamic (MHD) system has been introduced to test a sequential optimal interpolation assimilation technique that uses a Monte-Carlo method to calculate the forecast error covariance. An ensemble of 100 model runs with perturbed initial conditions are used to construct the covariance, and the assimilation algorithm is tested using Observation Simulation Experiments (OSE's). The system is run with a variety of observation types (magnetic and/or velocity fields) and a range of observation densities. The impact of cross covariances between velocity and magnetic fields is investigated by running the assimilation with and without these terms. Sets of twin experiments show that while observing both velocity and magnetic fields has the greatest positive impact on the system, observing the magnetic field alone can also effectively constrain the system. Observations of the velocity field are ineffective as a constraint on the magnetic field, even when observations are made at every point. The implications for geomagnetic data assimilation are discussed.
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spelling doaj.art-46fd5ba4dd1d4e36980e3ee44547bfde2022-12-21T23:21:07ZengCopernicus PublicationsNonlinear Processes in Geophysics1023-58091607-79462007-01-01142181192Data assimilation in a sparsely observed one-dimensional modeled MHD systemZ. SunA. TangbornA. TangbornW. KuangA one dimensional non-linear magneto-hydrodynamic (MHD) system has been introduced to test a sequential optimal interpolation assimilation technique that uses a Monte-Carlo method to calculate the forecast error covariance. An ensemble of 100 model runs with perturbed initial conditions are used to construct the covariance, and the assimilation algorithm is tested using Observation Simulation Experiments (OSE's). The system is run with a variety of observation types (magnetic and/or velocity fields) and a range of observation densities. The impact of cross covariances between velocity and magnetic fields is investigated by running the assimilation with and without these terms. Sets of twin experiments show that while observing both velocity and magnetic fields has the greatest positive impact on the system, observing the magnetic field alone can also effectively constrain the system. Observations of the velocity field are ineffective as a constraint on the magnetic field, even when observations are made at every point. The implications for geomagnetic data assimilation are discussed.http://www.nonlin-processes-geophys.net/14/181/2007/npg-14-181-2007.pdf
spellingShingle Z. Sun
A. Tangborn
A. Tangborn
W. Kuang
Data assimilation in a sparsely observed one-dimensional modeled MHD system
Nonlinear Processes in Geophysics
title Data assimilation in a sparsely observed one-dimensional modeled MHD system
title_full Data assimilation in a sparsely observed one-dimensional modeled MHD system
title_fullStr Data assimilation in a sparsely observed one-dimensional modeled MHD system
title_full_unstemmed Data assimilation in a sparsely observed one-dimensional modeled MHD system
title_short Data assimilation in a sparsely observed one-dimensional modeled MHD system
title_sort data assimilation in a sparsely observed one dimensional modeled mhd system
url http://www.nonlin-processes-geophys.net/14/181/2007/npg-14-181-2007.pdf
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