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Coastal ocean data assimilation using a multi-scale three-dimensional variational scheme

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Abstract

A multi-scale three-dimensional variational scheme (MS-3DVAR) is implemented to improve the effectiveness of the assimilation of both very sparse and high-resolution observations into models with resolutions down to 1 km. The improvements are realized through the use of background error covariances of multi-decorrelation length scales and by reducing the inherent observational representativeness errors. MS-3DVAR is applied to coastal ocean data assimilation to handle the wide range of spatial scales that exist in both the dynamics and observations. In the implementation presented here, the cost function consists of two components for large and small scales, and MS-3DVAR is implemented sequentially from large to small scales. A set of observing system simulation experiments (OSSEs) are performed to illustrate the advantages of MS-3DVAR over conventional 3DVAR in assimilating two of the most common types of observations—sparse vertical profiles and high-resolution surface measurements—simultaneously. One month of results from an operational implementation show that both the analysis error and bias are reduced more effectively when using MS-3DVAR.

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Acknowledgments

The research described in this publication was carried out, in part, at the Jet Propulsion Laboratory (JPL), California Institute of Technology, under a contract with the National Aeronautics and Space Administration (NASA). We gratefully acknowledge the multi-year support from Dr. Yi Chao, Remote Sensing Solutions, Inc., to the development of the MS-3DVAR system for the SCB region and also many discussions with him that help improve the system. This research was supported in part by the Office of Naval Research (N00014-12-1-093) and (N00014-10-1-0557). The authors thank the anonymous reviewers for comments that were very helpful in improving the manuscript.

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Authors and Affiliations

  1. Jet Propulsion Laboratory, California Institute of Technology, M/S 300-323, 4800 Oak Grove Drive, Pasadena, CA, USA

    Zhijin Li

  2. Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, CA, USA

    James C. McWilliams

  3. Department of Atmospheric and Oceanic Science, Center for Scientific Computation and Mathematical Modeling, Earth System Science Interdisciplinary Center, Institute for Physical Science and Technology, University of Maryland, College Park, MD, USA

    Kayo Ide

  4. Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, CA, USA

    John D. Farrara

Authors
  1. Zhijin Li
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  2. James C. McWilliams
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  3. Kayo Ide
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  4. John D. Farrara
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Correspondence to Zhijin Li.

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Responsible Editor: Emil Vassilev Stanev

This article is part of the Topical Collection on Coastal Ocean Forecasting Science supported by the GODAE OceanView Coastal Oceans and Shelf Seas Task Team (COSS-TT)

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Li, Z., McWilliams, J.C., Ide, K. et al. Coastal ocean data assimilation using a multi-scale three-dimensional variational scheme. Ocean Dynamics 65, 1001–1015 (2015). https://doi.org/10.1007/s10236-015-0850-x

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