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The future state of a dynamical model depends on control parameters such as initial conditions, model errors, empirical parameters of the model, and boundary conditions. Insufficient knowledge of these parameters leads to uncertainty of the prediction, which implies a probabilistic nature of the problem. The chaotic nature of nonlinear dynamical systems in weather and climate, and in geosciences in general, confirms the fundamentally probabilistic character of dynamical systems. Information about the dynamical state and its uncertainty is collected from observations. Blending the information from observations with information from dynamical models requires a coordinated effort in several areas of Physics and Mathematics: Probability Theory, Estimation Theory, Control Theory, Nonlinear Dynamics and Chaos/Information Theory. Since we are primarily interested in geosciences applications to high-dimensional dynamical systems, the computational component of the problem is also of great importance to our research.
Our research is encompassing all mentioned disciplines with the goal of developing a general methodology for uncertainty estimation of dynamical systems. At present, our focus is the development of the Maximum Likelihood Ensemble Filter (MLEF) with applications to weather, climate, and carbon cycle.
The next generation GOES satellites will begin with GOES-R, which is currently scheduled to launch in the year 2015. GOES-R will be equipped with the Advanced Baseline Imager (ABI), an imager that has significantly improved spectral, spatial and temporal resolution relative to the current GOES I-M series satellites. These improvements will greatly enhance our ability to make mesoscale weather, climate, oceanographic and environmental observations. Under the GOES-R research project and various other research projects at CIRA, we are employing and further developing an ensemble-based data assimilation method, referred to as the Maximum Likelihood Ensemble Filter (MLEF). For the GOES-R application, we are focusing on the MLEF capability to extract maximum information from the future GOES-R observations.