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.
Featured Article
Carrio, G.G., W.R. Cotton, D. Zupanski, and M. Zupanski
Abstract
A cloud-nucleating aerosol retrieval method was developed. It allows the estimation of ice-forming nuclei and cloud condensation nuclei (IFN and CCN) for regions in which boundary layer clouds prevail. The method is based on the assumption that the periodical assimilation of observations into a microscale model leads to an improved estimation of the model state vector (that contains the cloud-nucleating aerosol concentrations). The Colorado State University Cloud Resolving Model (CRM) version of the Regional Atmospheric Modeling System (RAMS@CSU) and the maximum likelihood ensemble filter algorithm (MLEF) were used as the forecast model and the assimilation algorithm, respectively. On the one hand, the microphysical modules of this CRM explicitly consider the nucleation of IFN, CCN, and giant CCN. On the other hand, the MLEF provides an important advantage because it is defined to address highly nonlinear problems, employing an iterative minimization of a cost function. This paper explores the possibility of using an assimilation technique with microscale models. These initial series of experiments focused on isolating the model response and showed that data assimilation enhanced its performance in simulating a mixed-phase Arctic boundary layer cloud. Moreover, the coupled model was successful in reproducing the presence of an observed polluted air mass above the inversion.
view full article
