Journal article
Ensemble Methods for Dynamic Data Assimilation of Chemical Observations in Atmospheric Models
JOURNAL OF ALGORITHMS & COMPUTATIONAL TECHNOLOGY, Vol.5(4), pp.667-692
2011
DOI: 10.1260/1748-3018.5.4.667
Abstract
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.
Details
- Title: Subtitle
- Ensemble Methods for Dynamic Data Assimilation of Chemical Observations in Atmospheric Models
- Creators
- A Sandu - Virginia TechE Constantinescu - Virginia TechG R Carmichael - University of IowaT F Chai - University of IowaD Daescu - Portland State UniversityJ H Seinfeld - California Institute of Technology
- Resource Type
- Journal article
- Publication Details
- JOURNAL OF ALGORITHMS & COMPUTATIONAL TECHNOLOGY, Vol.5(4), pp.667-692
- DOI
- 10.1260/1748-3018.5.4.667
- ISSN
- 1748-3026
- Language
- English
- Date published
- 2011
- Academic Unit
- Nursing; Chemical and Biochemical Engineering; Civil and Environmental Engineering
- Record Identifier
- 9984232159902771
Metrics
13 Record Views