World Library  

Add to Book Shelf
Flag as Inappropriate
Email this Book

Data Assimilation in a Sparsely Observed One-dimensional Modeled Mhd System : Volume 14, Issue 2 (14/05/2007)

By Sun, Z.

Click here to view

Book Id: WPLBN0004019816
Format Type: PDF Article :
File Size: Pages 12
Reproduction Date: 2015

Title: Data Assimilation in a Sparsely Observed One-dimensional Modeled Mhd System : Volume 14, Issue 2 (14/05/2007)  
Author: Sun, Z.
Volume: Vol. 14, Issue 2
Language: English
Subject: Science, Nonlinear, Processes
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


APA MLA Chicago

Kuang, W., Tangborn, A., & Sun, Z. (2007). Data Assimilation in a Sparsely Observed One-dimensional Modeled Mhd System : Volume 14, Issue 2 (14/05/2007). Retrieved from

Description: Department of Mathematics and Statistics, University of Maryland-Baltimore County, Baltimore, Maryland, USA. 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.

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

Bloxham, J., Gubbins, D., and Jackson, A.: Geomagnetic secular variation, Phil. Trans. Roy. Soc. Lond. A, 329, 415–502, 1989.; Borovikov, A., Rienecker, M. M., Keppene, C. L., and Johnson, C. C.: Multivariate error covariance estimates by Monte-Carlo simulation for assimilation studies in the Pacific Ocean, Monthly Weather Review, 133, 2310–2334, 2005.; Braginsky, S. I.: Magnetic waves in the Earth's core, Geomag. Aeron., 7, 851–859, 1967.; Braginsky, S. I.: Torsional magnetohydrodynamic vibrations in the Earth's core and variations in day length, Geomag. Aeron., 10, 1–8, 1976.; Braginsky, S. I.: Magnetic waves in the core of the Earth II, Geophy. Astrophy. Fluid Dyn., 14, 189–208, 1980.; Bunge, H. P., Richards, M. A., and Baumgardner, J. R.: Mantle-circulation models with sequaential data assimilation: Inferring present-day mantle structure from plate motion histories, Phil. Trans. Roy. Soc. Lond. A, 360, 2545–2567, 2002.; Cohn, S. E.: An introduction to estimation theory, J. Meteor. Soc. Japan, 75, 257–288, 1997.; Larmor, J.: How could a rotating body such as the Sun become a magnet, Rep. Brit. Assn. Advan. Sci., p. 159–160, 1919.; Constable, C. G., Johnson, C. L., and Lund, S. P.: Global magnetic field models for the past 3000 years:transient or permanent flux lobes?, Phil. Trans. Roy. Soc. Lond. A, 358, 991–1008, 2000.; Evensen, G.: Sequential data assimilation with a nonlinearquasi-geostrophic model using Monte Carlo methods to forecast error statistics, J. Geophys. Res.-Oceans, 99, 10 143–10 162, 1994.; Eymin, C.: \'Etude des mouvements à la surface du noyau terrestre, PhD thesis, Université Pierre et Marie Curie – Paris VI, 2004.; Gaspari, G. and Cohn, S. E.: Construction of correlation function in two and three dimensions, Q. J. R. Meteorol. Soc., 125, 723–757, 1999.; Guoyodo, Y. and Valet, J.-P.: Global changes in intensity of the Earth's magnetic field during the past 800kyr, Nature, 399, 249–252, 1999.; Jiang, W., Kuang, W., Fang, M., and Cox, C.: Understanding Time-Variable Gravity due to Core Dynamical Processes with Numerical Geodynamo Modeling, in: Dynamic Planet, edited by: Tregoning, Rizos, Springer, IAG 130, pp. 473–479, 2007.; Kalman, R. E.: A New Approach to Linear Filter and Prediction Problems, ASME, Ser., D, J. of Basic Engr., 98, 35–45, 1960.; Kono, M. and Roberts, P H.: Recent geodynamo simulations and observations of the geomagnetic field, Rev. Geophys., 40, 1–53, 2002.; Kuang, W. and Bloxham, J.: Numerical modeling of magnetohydrodyanmic convection in a rapidly rotating spherical shell: weak and strong field dynamo actions, J. Comp. Phys., 153, 51–81, 1999.; Kuang, W. and Chao, B F.: Geodynamo modeling and core-mantle interaction in the core-mantle boundary region, Geodynamics Series, 31, 193–212, 2003.; Kumar, S. and Roberts, P H.: A three dimensional kinematic dynamo, Proc. Roy. Soc. Lond. A, 314, 235–258, 1975.; Miller, R. N., Carter, E. F., and Blue, S. T.: Data assimilationinto non-linear stochastic models, Tellus, 51A, 167–194, 1999.; Roberts, P H. and Stix, M.: α-effect dynamos, by the Bullard-Gellman formalism, Astr. Astrophys., 18, 453–466, 1972.; Sabaka, T J., Olson, N., and Langel, R A.: A comprehensive model of the quiet-time near-Earth magnetic field; phase 3., Geophys. J. Int., 151, 32–68, 2002.; Tangborn, A.: Wavelet approximation of error covariance propagation in data assimilation, Tellus A, 56, 16–28, 2004.


Click To View

Additional Books

  • Estimation of Sedimentary Proxy Records ... (by )
  • A Lagrangian Approach to the Loop Curren... (by )
  • Tracking Heliospheric Disturbances by In... (by )
  • Earthquake Source Parameters Which Displ... (by )
  • Scaling Similarities of Multiple Fractur... (by )
  • An Update on Thorpex-related Research in... (by )
  • Closure of Multi-fluid and Kinetic Equat... (by )
  • Prediction of Minimum Temperatures in an... (by )
  • Long Range Predictability of Atmospheric... (by )
  • Deep Bore Well Water Level Fluctuations ... (by )
  • 3D Nonlinear Inversion by Entropy of Ima... (by )
  • Functional Background of the Tsallis Ent... (by )
Scroll Left
Scroll Right


Copyright © World Library Foundation. All rights reserved. eBooks from Hawaii eBook Library are sponsored by the World Library Foundation,
a 501c(4) Member's Support Non-Profit Organization, and is NOT affiliated with any governmental agency or department.