Optimal and scalable methods to approximate the solutions of large‐scale Bayesian problems: theory and application to atmospheric inversion and data assimilation-Reference-Cited by-同舟云学术

Optimal and scalable methods to approximate the solutions of large‐scale Bayesian problems: theory and application to atmospheric inversion and data assimilation

Published:2018-01 Issue:711 Volume:144 Page:365-390
ISSN:0035-9009
Container-title:Quarterly Journal of the Royal Meteorological Society
language:en
Short-container-title:Q.J.R. Meteorol. Soc.

Author:

Bousserez N.¹,Henze Daven K.¹

Affiliation:

1. Mechanical Engineering, University of Colorado Boulder, CO, USA

Funder

National Aeronautics and Space Administration

National Oceanic and Atmospheric Administration

National Science Foundation

University of Colorado Boulder

University of Colorado Denver

National Center for Atmospheric Research

Publisher

Wiley

Subject

Atmospheric Science

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/qj.3209

Reference60 articles.

1. An Ensemble Adjustment Kalman Filter for Data Assimilation

2. Ensemble–Variational Integrated Localized Data Assimilation

3. A review of forecast error covariance statistics in atmospheric variational data assimilation. I: Characteristics and measurements of forecast error covariances

4. Localization and the iterative ensemble Kalman smoother

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