State-of-the-art stochastic data assimilation methods for high-dimensional non-Gaussian problems-Reference-Cited by-同舟云学术

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State-of-the-art stochastic data assimilation methods for high-dimensional non-Gaussian problems

Published:2018-01-01 Issue:1 Volume:70 Page:1-43
ISSN:1600-0870
Container-title:Tellus A: Dynamic Meteorology and Oceanography
language:en
Short-container-title:Tellus A: Dynamic Meteorology and Oceanography

Author:

Vetra-Carvalho Sanita¹,van Leeuwen Peter Jan¹²,Nerger Lars³,Barth Alexander⁴,Altaf M. Umer⁵,Brasseur Pierre⁶,Kirchgessner Paul³,Beckers Jean-Marie⁴

Affiliation:

1. Department of Meteorology, University of Reading, Reading, UK.

2. National Centre for Earth Observation, Reading, UK.

3. Alfred Wegener Institute, Helmholtz Center for Polar and Marine Research, Bremerhaven, Germany.

4. GeoHydrodynamic and Environmental Research (GHER), University of Liège, Liège, Belgium.

5. King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.

6. CNRS, IRD, Grenoble INP, IGE, University of Grenoble Alpes, Grenoble, France.

Funder

FP7 Space

Publisher

Informa UK Limited

Subject

Atmospheric Science,Oceanography

Link

https://www.tandfonline.com/doi/pdf/10.1080/16000870.2018.1445364

Reference121 articles.

1. An exploration of the equivalent weights particle filter

2. A Local Least Squares Framework for Ensemble Filtering

3. An Ensemble Adjustment Kalman Filter for Data Assimilation

4. Exploring the need for localization in ensemble data assimilation using a hierarchical ensemble filter

5. Spatially and temporally varying adaptive covariance inflation for ensemble filters

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