New data sources are beginning to have a dramatic impact on our ability to understand the earth as an integrated system. Our prospects for dealing with the environmental issues of the 21st century -- climate change, population pressures on natural resources, and major modifications in global element cycles -- depend largely on this new information. However, our ability to process and interpret environmental data is not keeping pace with the dramatic increase in available information, especially information from airborne and orbital remote sensing platforms. If we are to realize the potential benefits of new sensing technologies we will need to develop intelligent environmental data assimilation procedures that are able to efficiently extract useful information about the earth from a diverse set of data sources.
Environmental data assimilation can be posed as a problem of estimating a large number of unobservable or highly uncertain variables (e.g. sea surface heights, atmospheric pressures, hydrologic fluxes, etc.) from a large number of related but noisy measurements (e.g. microwave radiances or backscatter detected by a satellite sensor). The estimation procedure relies on mathematical models that relate unknowns to measurements. Environmental estimation problems are challenging because the systems of interest: 1) are spatially distributed and highly variable over a wide range of space and time scales, 2) are difficult to describe with precision, 3) are often nonlinear, even chaotic, and 4) are often characterized by non-unique relationships between unknowns and measurements.
This project is concerned with very large problems (many measurements and many unknowns) which are not amenable to traditional data assimilation techniques but are of crucial interest to researchers in the earth sciences. An interdisciplinary team will develop a better understanding of the issues of dimensionality reduction and uncertainty propagation that are crucial to large-scale data assimilation. So-called ensemble methods provide a particularly informative way to identify these key features. A new generation of "intelligent" data assimilation methods will be developed that build on the understanding gained from the reduced problem. The applicability of these methods will be investigated on problems of broad interest in the earth sciences, including problems that 1) deal with coupled systems, 2) cut across traditional disciplines, and 3) work with remote sensing data sets.
This ITR project brings together acknowledged experts on environmental data assimilation. It is a group ITR project, rather than several individual projects, which cuts across earth science disciplines. The research will be coordinated with: 1) a seminar series, 2) joint supervision of Ph.D. students and post-doctoral researchers, 3) a Ph.D. mentoring program, 4) a selection of cross-cutting sample problems, and 5) co-authored publications.
