11 Jan 2023
11 Jan 2023
Status: this preprint is currently under review for the journal NPG.

Towards Strongly-coupled Ensemble Data Assimilation with Additional Improvements from Machine Learning

Eugenia Kalnay1,2, Travis Sluka3, Takuma Yoshida4, Cheng Da5,6, and Safa Mote1,2 Eugenia Kalnay et al.
  • 1Department of Atmospheric and Oceanic Science, University of Maryland, College Park, College Park, Maryland
  • 2Institute for Physical Science and Technology, University of Maryland, College Park, College Park, Maryland
  • 3Joint Center for Satellite Data Assimilation, University Corporation for Atmospheric Research, Boulder, Colorado
  • 4Numerical Prediction Development Center, Japan Meteorological Agency, Japan
  • 5Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland
  • 6NASA Global Modeling and Assimilation Office (GMAO), Greenbelt, Maryland

Abstract. We assessed different coupled data assimilation strategies with a hierarchy of coupled models, ranging from the simple coupled Lorenz model to the state-of-the-art coupled general circulation model CFSv2. With the coupled Lorenz model, we assessed the analysis accuracy by strongly-coupled Ensemble Kalman Filter (EnKF) and 4D-Variational (4D-Var) methods with varying assimilation window lengths. The analysis accuracy of the strongly-coupled EnKF with a short assimilation window is comparable to that of 4D-Var with a long assimilation window. For 4D-Var, the strongly-coupled approach with the coupled model produces more accurate ocean analysis than the ECCO-like approach using the uncoupled ocean model. Experiments with the coupled quasi-geostrophic model conclude that the strongly-coupled approach outperforms the weakly-coupled and uncoupled approaches for both the full-rank EnKF and 4D-Var, with the strongly-coupled EnKF and 4D-Var showing a similar level of accuracy higher than other coupled data assimilation approaches such as the outer loop coupling. A strongly-coupled EnKF software framework is developed and applied to the intermediate-complexity coupled model SPEEDY-NEMO and the state-of-the-art operational coupled model CFSv2. Experiments assimilating synthetic or real atmospheric observations into the ocean through strongly-coupled EnKF show that the strongly-coupled approach improves the analysis of the atmosphere and upper oceans, but degrades observation fits in the deep ocean, probably due to the unreliable error correlation estimated by a small ensemble. The correlation-cutoff method is developed to reduce the unreliable error correlations between physically irrelevant model states and observations. Experiments with the coupled Lorenz model demonstrate that strongly-coupled EnKF informed by the correlation-cutoff method produces more accurate coupled analyses than the weakly-coupled and plain strongly-coupled EnKF regardless of the ensemble size. To extend the correlation-cutoff method to operational coupled models, a neural network approach is proposed to systematically acquire the observation localization functions for all pairs between the model state and observation types. The following strongly-coupled EnKF experiments with an intermediate-complexity coupled model show promising results with this method.

Eugenia Kalnay et al.

Status: open (until 08 Mar 2023)

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Eugenia Kalnay et al.

Eugenia Kalnay et al.


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Short summary
We reviewed our progress on strongly-coupled ensemble data assimilation with a hierarchy of coupled models, ranging from the simple coupled Lorenz model to the state-of-the-art coupled general circulation model CFSv2. We showed that strongly-coupled ensemble data assimilation provides the most accurate coupled analyses if using a sufficient ensemble. When the ensemble size is insufficient, applying the correlation-cutoff method can significantly improve the strongly-coupled analyses.