Mathematical Approaches for Data Assimilation of Atmospheric Constituents and Inverse Modeling (Cancelled) (20w5166)


Richard Menard (Environment and Climate Change Canada)

(Ecole des Ponts ParisTech)

(University of Maryland)

(University of Toronto)


The Banff International Research Station will host the "Mathematical Approaches for Data Assimilation of Atmospheric Constituents and Inverse Modeling" workshop in Banff from October 18 to October 23, 2020.

As the world population lives more and more in large urban areas, and as we are about to reach critical levels of greenhouse gases concentrations, the changing atmospheric composition has increasingly important economic, environmental and health impacts. It is thus becoming important to better quantify air pollution and its sources, using all the available information from observations to computer models, and use it in a synergistic way to maximize the information content – this is what data assimilation and inverse modeling aim for.

This interdisciplinary workshop brings together engineers and researchers from numerical mathematics, statistics, and environmental sciences, to develop and innovate on the assimilation and inverse methods to address the specific issues related to atmospheric composition and chemistry. It will also be a forum to train new scientists in this emerging field, and to promote the research towards new operational monitoring products.

The Banff International Research Station for Mathematical Innovation and Discovery (BIRS) is a collaborative Canada-US-Mexico venture that provides an environment for creative interaction as well as the exchange of ideas, knowledge, and methods within the Mathematical Sciences, with related disciplines and with industry. The research station is located at The Banff Centre in Alberta and is supported by Canada's Natural Science and Engineering Research Council (NSERC), the U.S. National Science Foundation (NSF), Alberta's Advanced Education and Technology, and Mexico's Consejo Nacional de Ciencia y Tecnología (CONACYT).