Collaborative Research: CMG: Adaptive High-Order Methods for Nonhydrostatic Numerical Weather Prediction

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This project is a new collaboration between geoscientists and mathematicians at two locations: Norman, Oklahoma and Boulder, Colorado. The initiative brings scientists new to the atmospheric science community to aid in the development of parallel, adaptive, unstructured, high-order methods for atmospheric modeling. Established atmospheric modelers will guide the effort, offering their experience with numerical modeling of complex weather systems. The collaboration also brings together the considerable expertise and resources of the National Center for Atmospheric Research (NCAR) located in Boulder, Colorado and the National Severe Storms Laboratory (NSSL) located in Norman, Oklahoma. The NCAR effort will build on its experience of developing high-order adaptive spectral element solvers for the shallow water equations, moving to high-order finite-volume adaptive Discontinuous Galerkin methods for the nonhydrostatic equation set. The NSSL effort will aid in this development by addressing the adaptivity and parallel computing issues and testing the methods on a suite of test problems of increasing complexity with a central long term goal of modeling tornadoes.Weather simulation is a complex problem involving many different scalesand requiring high resolution solutions of the equations governing the Earth's atmosphere. Recently, it has been recognized that localized flow structures, such as storms, may play an important role in obtaining the best weather forecast. Higher resolution weather simulations are required in applications in the atmospheric and air quality communities, at operational centers around the country attempting to forecast significant weather events, and for research endeavors that focus on the dynamics of severe storms such as hurricanes or tornadoes. Recent developments in computational mathematics techniques may be applicable to these models in order to tailor them to treatment of local structures such as storms and fronts. This project aims to create a collaboration between mathematicians who have developed these methods and meteorologists who have modeled and studied such events, that will yield more efficient and accurate models. Across the globe weather modelers are striving to increase resolution for the models, but are limited in their methodology to only reducing the spacing, thereby increasing computer time significantly. The US is already behind in that race. This project would leapfrog that strategy by developing more efficient models that can automatically hone in on localized structures with high precision.