CMG Collaborative Research: Non-assimilation Fusion of Data and Models

University Of Southern California, Los Angeles CA

Investigators

Abstract

The PIs will develop a methodology for improving estimate and prediction of the state of a dynamical system, with particular focus on analyzing ocean dynamics. The primary goals of this project are thus to develop innovative approaches for representation and manipulation of data uncertainty and model error using a fuzzy set formulation and to then apply these approaches for the data and model fusion formulated as the global optimization problem. Convenient and fast numerical algorithms will be developed to solve the problem using high-performance parallel computing. Such an approach differs from usual statistical estimates but with advantages and drawbacks of its own. The general mathematical theory will be applied to a long-standing but important problem of improving estimates and prediction of the state of the ocean. In particular, the proposed study targets a synthesis of submesoscale/mesoscale fronts, jets and eddies by fusing satellite observations, float and shipboard data of lower resolution, as well as ROMS simulation results for Central California. The theory should provide new tools to be applied in oceanography, meteorology, climatology, artificial intelligence, computer science, control engineering, decision theory, expert systems, operational research and pattern recognition. As the first step in using these tools for broader oceanography community goals, the fusion approach will be applied to different data bases to understand and quantify heat storage and carbon content of the North Atlantic in collaboration with scientists from Great Britain and Germany and to allow junior scientists to obtain excellent training and learning in cross disciplinary/multi-disciplinary areas of great scientific and practical importance. The PIs will address a long-standing but important problem involved with improving the estimation and prediction of the state of the ocean. The primary goals of this project are to develop an innovative approach for representation and manipulation of uncertainty coming from a wide variety of sources such as sensor outputs, model outputs, aggregating expert opinions as well as merging different databases and data even when distinct pieces of information are contradictory, and to suggest methods to fuse this information in decision making goals. The study will provide new mathematical theory and tools relevant for this problem, but also for more general applications in oceanography, meteorology and climatology. Mathematically the approach uses a fuzzy set formulation which originated in pure mathematics and which will be adapted for representing and manipulating data uncertainty and ocean model error. Results of the work will advance development of new forecast metrics in terms of fuzzy sets as well as new methods for quantification of model predictability through data-model and model-model comparisons at weather and climatic scales. As the first step in using these tools for broader oceanography community goals, the approach will be applied to different data bases which relate to quantifying heat storage and carbon content of the North Atlantic. The PIs will collaborate with scientists from Great Britain and Germany. Junior scientists involved in the project will obtain excellent training and learning in cross disciplinary/multi-disciplinary areas of great scientific and practical importance.