A Hybrid Monte‐Carlo sampling smoother for four‐dimensional data assimilation-Reference-Cited by-同舟云学术

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A Hybrid Monte‐Carlo sampling smoother for four‐dimensional data assimilation

Published:2016-06-14 Issue:1 Volume:83 Page:90-112
ISSN:0271-2091
Container-title:International Journal for Numerical Methods in Fluids
language:en
Short-container-title:Int. J. Numer. Meth. Fluids

Author:

Attia Ahmed¹,Rao Vishwas¹,Sandu Adrian¹

Affiliation:

1. Computational Science Laboratory Virginia Polytechnic Institute and State University Blacksburg VA USA

Funder

Air Force Office of Scientific Research

National Science Foundation

Publisher

Wiley

Subject

Applied Mathematics,Computer Science Applications,Mechanical Engineering,Mechanics of Materials,Computational Mechanics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/fld.4259

Reference49 articles.

1. AN ERROR SUBSPACE PERSPECTIVE ON DATA ASSIMILATION

2. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics

3. Ensemble Data Assimilation without Perturbed Observations

4. An Ensemble Adjustment Kalman Filter for Data Assimilation

5. Adaptive Sampling with the Ensemble Transform Kalman Filter. Part I: Theoretical Aspects

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