TY - GEN
AU - Angela Benedetti
AU - Mike Fisher
AB - 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 four–dimensional variational (4D–Var) assimilation was started in March 2005 at the European Centre for Medium–Range Weather Fore-casts (ECMWF). Among the main building blocks of the system, an error background covariance matrix for aerosol mixing ratio was built based on background statistics constructed from aerosol forecasts. These statistics were generated from the differences between the 48h and 24h 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 vari-ables. This is the .rst time, to our knowledge, that it is applied to a novel variable such as aerosol mixing ratio, in the context of a global model. Aerosol assimilation systems have been successfully run with ei-ther 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 correla-tions are modelled through a 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 total aerosol mixing ratio.
BT - ECMWF Technical Memoranda
CY - Shinfield Park, Reading
DA - 06/2006
DO - 10.21957/vovnzah16
LA - eng
M1 - 489
N2 - 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 four–dimensional variational (4D–Var) assimilation was started in March 2005 at the European Centre for Medium–Range Weather Fore-casts (ECMWF). Among the main building blocks of the system, an error background covariance matrix for aerosol mixing ratio was built based on background statistics constructed from aerosol forecasts. These statistics were generated from the differences between the 48h and 24h 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 vari-ables. This is the .rst time, to our knowledge, that it is applied to a novel variable such as aerosol mixing ratio, in the context of a global model. Aerosol assimilation systems have been successfully run with ei-ther 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 correla-tions are modelled through a 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 total aerosol mixing ratio.
PB - ECMWF
PP - Shinfield Park, Reading
PY - 2006
EP - 21
T2 - ECMWF Technical Memoranda
TI - Background error statistics for aerosols
UR - https://www.ecmwf.int/node/8059
ER -