Dynamical interpolation of surface ocean chlorophyll fields via data assimilation with an iterative ensemble smoother
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Inference of the sea surface chlorophyll field from incomplete satellite coverage is posed as a formal inverse problem using a Monte Carlo approach to Bayesian estimation. We introduce a new method, the strong constraint iterative ensemble smoother, for solving the general coupled physical-biological parameter estimation problem where model nonlinearities may be relevant. The forward model is posed in four ways: (1) advection-diffusion, (2) linear advection-diffusion-reaction, (3) nonlinear advection-diffusion-reaction, and (4) a nonlinear nutrient-phytoplankton model. Hindcast skill is demonstrated through analysis of the fit to independent data in a series of experiments utilizing MODIS chlorophyll imagery from the Middle Atlantic Bight during summer of 2006. The data assimilative model demonstrates skill over a range of presumed observational error. Both the purely physical model (advection-diffusion only) and the coupled physical-biological models exhibit skill fitting unassimilated data. The skill of the coupled physical-biological models is greater than the skill of the advection-diffusion model, owing at least in part to greater degrees of freedom in those inversions. (C) 2011 Elsevier B.V. All rights reserved.