EAKF-CMAQ: Introduction and evaluation of a data assimilation for CMAQ based on the ensemble adjustment Kalman filter

A Zubrow, L Chen, VR Kotamarthi

Research output: Contribution to journalArticle (Academic Journal)

9 Citations (Scopus)

Abstract

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.
Translated title of the contributionEAKF-CMAQ: Introduction and evaluation of a data assimilation for CMAQ based on the ensemble adjustment Kalman filter
Original languageEnglish
Number of pages18
JournalJournal of Geophysical Research: Atmospheres
Volume113, D09302
DOIs
Publication statusPublished - May 2009

Fingerprint Dive into the research topics of 'EAKF-CMAQ: Introduction and evaluation of a data assimilation for CMAQ based on the ensemble adjustment Kalman filter'. Together they form a unique fingerprint.

  • Cite this