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Tellus A: Dynamic Meteorology and Oceanography Volume 62, 2010 - Issue 3
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Original Articles

A hybrid approach to estimating error covariances in variational data assimilation

Haiyan ChengComputational Science Laboratory, Department of Computer Science, Virginia Polytechnic Institute and State University, 2202 Kraft Drive, Blacksburg, VA24060, USA;
,
Mohamed JardakComputational Science Laboratory, Department of Computer Science, Virginia Polytechnic Institute and State University, 2202 Kraft Drive, Blacksburg, VA24060, USA;
,
Mihai AlexeComputational Science Laboratory, Department of Computer Science, Virginia Polytechnic Institute and State University, 2202 Kraft Drive, Blacksburg, VA24060, USA;
&
Adrian SanduComputational Science Laboratory, Department of Computer Science, Virginia Polytechnic Institute and State University, 2202 Kraft Drive, Blacksburg, VA24060, USA;Correspondence[email protected]
Pages 288-297 | Received 02 Apr 2009, Accepted 02 Mar 2010, Published online: 15 Dec 2016

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H. Ngodock, I. Souopgui, M. Carrier, S. Smith, J. Osborne & J. D’Addezio. (2020) An ensemble of perturbed analyses to approximate the analysis error covariance in 4dvar. Tellus A: Dynamic Meteorology and Oceanography 72:1, pages 1-12.
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Ulrich Römer, Mahesh Narayanamurthi & Adrian Sandu. (2018) Solving parameter estimation problems with discrete adjoint exponential integrators. Optimization Methods and Software 33:4-6, pages 750-770.
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Bin Zhang, Xiangjun Tian, Jianhua Sun, Feng Chen, Yuanchun Zhang, Lifeng Zhang & Shenming Fu. (2015) PODEn4DVar-based radar data assimilation scheme: formulation and preliminary results from real-data experiments with advanced research WRF (ARW). Tellus A: Dynamic Meteorology and Oceanography 67:1.
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Xiangjun Tian & Xiaobing Feng. (2015) A non-linear least squares enhanced POD-4DVar algorithm for data assimilation. Tellus A: Dynamic Meteorology and Oceanography 67:1.
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