Ensemble data assimilation applications to atmospheric and carbon cycle science
Dusanka Zupanski, CIRA/Colorado State University, Fort Collins, CO; and S. A. Denning and M. Uliasz
Data assimilation methods are interdisciplinary in nature since problems involving dynamical models and observations are often shared across different scientific disciplines (e.g., atmospheric, oceanic, hydrological, and carbon cycle sciences). Sharing the knowledge and experience from different applications of data assimilation methods is, therefore, of fundamental importance for gaining new knowledge and for further improvements of data assimilation methods.
Toward this goal, this study examines applications of an ensemble based data assimilation method entitled Maximum Likelihood Ensemble Filter (MLEF) in two different areas: atmospheric and carbon transport research. One of the most significant differences between these two applications is in the ways the forecast error covariance (i.e., prior error information) is used. This difference arises from different dynamical models used to propagate forecast error covariance from one data assimilation cycle to another. Additional differences include different special and time scales and different typical number of degrees of freedom. Experimental results employing the MLEF approach to atmospheric and carbon transport models will be examined and discussed.
Extended Abstract (580K)
Session 6, Assimilation of Observations (Ocean, Atmosphere, and Land Surface) into Models: Assimilation Methods; Minimization Techniques; Forward Models and Their Adjoints; Incorporation of Constraints; Error Statistics
Wednesday, 1 February 2006, 8:30 AM-12:00 PM, A405
Previous paper Next paper
Browse or search entire meeting
AMS Home Page