Particle filter-based data assimilation for a three-dimensional biological ocean model and satellite observations

Authors

  • Jann Paul Mattern,

    Corresponding author
    1. Department of Mathematics and Statistics, Dalhousie University, Halifax, Nova Scotia, Canada
    2. Department of Oceanography, Dalhousie University, Halifax, Nova Scotia, Canada
    • Corresponding author: J. P. Mattern, Department of Mathematics and Statistics, Dalhousie University, Halifax, NS B3H 4R2, Canada. (paul.mattern@dal.ca)

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  • Michael Dowd,

    1. Department of Mathematics and Statistics, Dalhousie University, Halifax, Nova Scotia, Canada
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  • Katja Fennel

    1. Department of Oceanography, Dalhousie University, Halifax, Nova Scotia, Canada
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Abstract

[1] We assimilate satellite observations of surface chlorophyll into a three-dimensional biological ocean model in order to improve its state estimates using a particle filter referred to as sequential importance resampling (SIR). Particle Filters represent an alternative to other, more commonly used ensemble-based state estimation techniques like the ensemble Kalman filter (EnKF). Unlike the EnKF, Particle Filters do not require normality assumptions about the model error structure and are thus suitable for highly nonlinear applications. However, their application in oceanographic contexts is typically hampered by the high dimensionality of the model's state space. We apply SIR to a high-dimensional model with a small ensemble size (20) and modify the standard SIR procedure to avoid complications posed by the high dimensionality of the model state. Two extensions to the SIR include a simple smoother to deal with outliers in the observations, and state-augmentation which provides the SIR with parameter memory. Our goal is to test the feasibility of biological state estimation with SIR for realistic models. For this purpose we compare the SIR results to a model simulation with optimal parameters with respect to the same set of observations. By running replicates of our main experiments, we assess the robustness of our SIR implementation. We show that SIR is suitable for satellite data assimilation into biological models and that both extensions, the smoother and state-augmentation, are required for robust results and improved fit to the observations.

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