Estimating Forecast Error Covariances for Strongly Coupled Atmosphere–Ocean 4D-Var Data Assimilation
Author:
Affiliation:
1. School of Mathematical and Physical Sciences, and National Centre for Earth Observation, University of Reading, Reading, United Kingdom
Abstract
Funder
Natural Environment Research Council
Publisher
American Meteorological Society
Subject
Atmospheric Science
Link
http://journals.ametsoc.org/doi/pdf/10.1175/MWR-D-16-0284.1
Reference28 articles.
1. A review of forecast error covariance statistics in atmospheric variational data assimilation. I: Characteristics and measurements of forecast error covariances
2. The representation of the analysis effect in three error simulation techniques
3. On the use of EDA background error variances in the ECMWF 4D-Var
4. The evolution of the ECMWF hybrid data assimilation system
5. Intercomparison of Variational Data Assimilation and the Ensemble Kalman Filter for Global Deterministic NWP. Part I: Description and Single-Observation Experiments
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3. Review article: Towards strongly coupled ensemble data assimilation with additional improvements from machine learning;Nonlinear Processes in Geophysics;2023-06-28
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