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Activity Number:
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372
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 1, 2007 : 8:30 AM to 10:20 AM
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Sponsor:
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IMS
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| Abstract - #309203 |
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Title:
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Statistical Data Assimilation for Marine Ecological Prediction
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Author(s):
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Michael Dowd*+
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Companies:
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Dalhousie University
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Address:
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, Halifax, NS, B3H 4J1, Canada
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Keywords:
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nonlinear dynamics ; Monte Carlo methods ; Bayesian statistics ; state space models ; oceanography ; ecosystem models
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Abstract:
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Ocean prediction relies on dynamical models for biological and physical processes, and available time series oceanographic measurements. In this presentation, I overview statistical approaches for combining observations with nonlinear differential equation based mathematical models in the marine environmental sciences. This problem known as data assimilation and has the goal of joint estimation of the time evolving system state, as well as static parameters. The approach taken relies on the nonlinear and non-Gaussian state space modeling framework. Solution techniques involve Monte Carlo methods to approximate the target densities and to treat filtering, smoothing and likelihood based parameter estimation. These ideas are considered for operational ocean prediction using a dynamical marine ecosystem model and near real-time observations from a coastal ocean observing system.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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