Title: Ensemble Strategies for State-Parameters Estimation in Ocean Ecosystem Models

Author: Mohamad El Gharamti (Nansen Environmental and Remote Sensing Center, King Abdullah University of Science and Technology)
Laurent Bertino (Nansen Environmental and Remote Sensing Center)
Boujemaa Ait-El-Fquih (King Abdullah University of Science and Technology)
Annette Samuelsen (Nansen Environmental and Remote Sensing Center)
Ibrahim Hoteit (King Abdullah University of Science and Technology)

Given the recent strong international focus on developing new data assimilation systems for biological models, we present in this comparative study the application of newly developed state-parameters estimation tools to an ocean ecosystem model. It is quite known that the available physical models are still too simple compared to the complexity of the ocean biology. Furthermore, various biological parameters remain poorly unknown and hence wrong specifications of such parameters can lead to large model errors.

Standard joint state-parameters augmentation technique using the ensemble Kalman filter (Stochastic EnKF) has been extensively tested in many geophysical applications. Some of these assimilation studies reported that jointly updating the state and the parameters might introduce significant inconsistency especially for strongly nonlinear models. This is usually the case for ecosystem models particularly during the period of the spring bloom. A better handling of the estimation problem is often carried out by separating the update of the state and the parameters using the so-called Dual EnKF. The dual filter is computationally more expensive than the Joint EnKF but is expected to perform more accurately. Using a similar separation strategy, we propose a new EnKF estimation algorithm in which we apply a one-step-ahead smoothing to the state. The new state-parameters estimation scheme is derived in a consistent Bayesian filtering framework and results in separate update steps for the state and the parameters. Unlike the classical filtering path, the new scheme starts with an update step and later a model propagation step is performed.

We test the performance of the new scheme against the Joint and the Duals EnKFs in a 1D ecosystem model. We use concentration measurements of nutrients to estimate different biological parameters of phytoplanktons and zooplanktons. We analyze the performance of the filters in terms of complexity and accuracy of the state and parameters estimates.

GMAO Head: Steven Pawson Global Modeling and Assimilation Office NASA Goddard Space Flight Center

Curator: Nikki Privé Last Updated: Feb 9 2015