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EAKF-CMAQ: Introduction and evaluation of a data assimilation for CMAQ based on the ensemble adjustment Kalman filter
Institute for the Environment, University of North Carolina–Chapel Hill, Chapel Hill, North Carolina, USA
Department of Mathematics, University of Bristol, Bristol, UK
Environmental Science Division, Argonne National Laboratory, Argonne, Illinois, USA
A new approach is presented for data assimilation using the ensemble adjustment Kalman filter (EAKF) technique for surface measurements of carbon monoxide in a single tracer version of the community air quality model. An implementation of the EAKF known as the Data Assimilation Research Testbed at the National Center for Atmospheric Research was used for developing the model. Three different sets of numerical experiments were performed to test the effectiveness of the procedure and the range of key parameters used in implementing the procedure. The model domain includes much of the northeastern United States. The first two numerical experiments use idealized measurements derived from defined model runs, and the last test uses measurements of carbon monoxide from approximately 220 Air Quality System monitoring sites over the northeastern United States, maintained by the U.S. Environmental Protection Agency. In each case, the proposed method provided better results than the method without data assimilation.
Received 10 August 2007; accepted 5 February 2008; published 10 May 2008.
Citation: (2008), EAKF-CMAQ: Introduction and evaluation of a data assimilation for CMAQ based on the ensemble adjustment Kalman filter, J. Geophys. Res., 113, D09302, doi:10.1029/2007JD009267.
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