Background error statistics for aerosols

doi:10.1002/qj.37

2007-03-21

PRE

Angela Benedetti *

Michael Fisher

DOI：10.1002/qj.37

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摘要

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As part of the European initiative for Global Monitoring for Environment and Security (GMES), the Global and regional Earth-system Monitoring using Satellite and in situ data (GEMS) project has been funded to provide forecasts and analysis of atmospheric constituents Such as aerosols, and greenhouse and reactive gases. In this context, the development of an aerosol analysis system based on a four-dimensional variational (4D-Var) assimilation was started in March 2005 at the European Centre for Medium-Range Weather Forecasts (ECMWF). As part of the main building blocks of the system, an error background covariance matrix for aerosol mixing ratio was built based oil background statistics constructed from aerosol forecasts. These statistics were generated from the differences between the 48-hour and 24-hour forecasts of aerosol mixing ratios for sea salt, desert dust and continental particulate, Using the NMC method. This method has been widely used by many numerical weather prediction centres to build background error statistics for state variables. This is the first time, to Our knowledge, that it has been applied to a novel variable such as an aerosol mixing ratio, in the context of a global model. Aerosol assimilation systems have been successfully run with either observationally-derived or model-prescribed error covariance matrices with pre-assigned correlation lengths. In this study, the aerosol vertical and horizontal structures of these error correlations as derived with the aid of a state-of-the-art model are investigated and discussed in depth. Successively these error correlations are modelled through,I spectral/wavelet approach and used to construct a background error covariance matrix. Application of this background matrix in the context of a single observation 4D-Var experiment produced Successful preliminary analyses of the total aerosol mixing ratio. Copyright (c) 2007 Royal Meteorological Society.

Keyword:

aerosol assimilation

background error covariance matrix

aerosol error statistics