Data assimilation with a local Ensemble Kalman Filter applied to a three-dimensional biological model of the Middle Atlantic Bight

Abstract

A multivariate sequential data assimilation approach, the Localized Ensemble Kalman Filter (LEnKF), was used to assimilate daily satellite observations of ocean chlorophyll into a three-dimensional physical–biological model of the Middle Atlantic Bight (MAB) for the year 2006. Covariance localization was applied to make the EnKF analysis more effective by removing spurious long-range correlations in the ensemble approximation of the model's covariance. The model is based on the Regional Ocean Modeling System (ROMS) and coupled to a biological nitrogen cycle model, which includes seven state variables: chlorophyll, phytoplankton, nitrate, ammonium, small and large detrital nitrogen, and zooplankton. An ensemble of 20 model simulations, generated by perturbing the biological parameters according to assumed probability distributions, was used. Model fields of chlorophyll, phytoplankton, nitrate and zooplankton were updated at all vertical layers during LEnKF analysis steps, based on their cross-correlations with surface chlorophyll (the observed variable). The performance of the LEnKF scheme, its influence on the model's predictive skill and on surface particulate organic matter concentrations and primary production are investigated. Estimates of surface chlorophyll and particulate organic carbon are improved in the data-assimilative simulation when compared to one without any assimilation, as is the model's predictive skill.