Premium
Assimilation of GOME ozone profiles and a global chemistry–transport model using a Kalman filter with anisotropic covariance
Author(s) -
Segers A. J.,
Eskes H. J.,
Van Der A R. J.,
Van Oss R. F.,
Van Velthoven P. F. J.
Publication year - 2005
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1256/qj.04.92
Subject(s) - data assimilation , covariance , ozone , parametrization (atmospheric modeling) , environmental science , kalman filter , tropospheric ozone , atmospheric sciences , meteorology , mathematics , geology , physics , statistics , quantum mechanics , radiative transfer
Nadir ozone profiles retrieved from the Global Ozone Monitoring Experiment (GOME) instrument are assimilated with a global three‐dimensional (3D) atmospheric ozone model. The assimilation procedure is based on the Kalman filter equations, and is an extension of an existing assimilation procedure for total ozone columns. As a novelty, a 3D covariance model is developed using a single parametrization for correlations in all directions, instead of the usually applied separation in horizontal and vertical directions. The parametrization is anisotropic in all directions, accounting for the different correlation lengths of ozone with respect to altitude, latitude, and longitude. The assimilation procedure includes full use of the averaging kernel information provided with the GOME retrieval product. The averaging kernels account for the smaller sensitivity of the GOME instrument below the ozone maximum and the limited vertical resolution. A singular‐value decomposition of the kernels is used to reduce the large data volume. A one‐year dataset of GOME ozone profiles is assimilated for the year 2000. Independent data from ozonesondes are used to validate the results. A case‐study shows that the assimilation of GOME profiles is able to improve the simulation of the vertical ozone distribution even in the case of strong vertical gradients. Copyright © 2005 Royal Meteorological Society.