This work was supported by the National Science Foundation through the award NSF ITR AP&IM 0205198 managed by Dr. Frederica Darema.
Journal of Algorithms & Computational Technology
Atmospheric models -- Data processing, Stochastic processes, Kalman filtering, Simulation methods
The task of providing an optimal analysis of the state of the atmosphere requires the development of dynamic data-driven systems (DDDAS) that efficiently integrate the observational data and the models. Data assimilation, the dynamic incorporation of additional data into an executing application, is an essential DDDAS concept with wide applicability. In this paper we discuss practical aspects of nonlinear ensemble Kalman data assimilation applied to atmospheric chemical transport models. We highlight the challenges encountered in this approach such as filter divergence and spurious corrections, and propose solutions to overcome them, such as background covariance inflation and filter localization. The predictability is further improved by including model parameters in the assimilation process. Results for a large scale simulation of air pollution in North-East United States illustrate the potential of nonlinear ensemble techniques to assimilate chemical observations.
Sandu, A., Constantinescu, E., Carmichael, G. R., Chai, T., Daescu, D., & Seinfeld, J. H. (2011). Ensemble methods for dynamic data assimilation of chemical observations in atmospheric models. Journal of Algorithms & Computational Technology, 5(4), 667-692.
Originally appeared in Journal of Algorithms & Computational Technology, volume 5, number 4. May be accessed at https://doi.org/10.1260/1748-3018.5.4.667. Published by SAGE Publications.