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Sensitivity analysis for the assimilation procedure of satellite-based aerosol measurements in a chemical transport model using aerosol component information

Dmytro Martynenko(1), Thomas Holzer-Popp(1) and Marion Schroedter-Homscheidt(1)

(1) German Aerospace Centre, Oberpfaffenhofen, 82234 Wessling, Germany

Abstract

Aerosol monitoring is of growing interest due to the impact of aerosol particle concentration on human health and the global climate. The key question of this paper is to understand how the assimilation of satellite atmospheric aerosol observations with enhanced observation and background covariance matrices improves the capability of a chemical transport model in reproducing the distribution of tropospheric particles. The second task of this study is a comparison of results for assimilation between improved covariance matrices with the help if information content analysis of satellite data. This comparison is made with the case without tuning of observation and background covariance matrices. The study is carried out using the Model for Atmospheric Transport and Chemistry (MATCH). As measurement input vector for the assimilation procedure satellite data from GOME-2 and AVHRR instruments onboard MetOp or from SCIAMACHY and AATSR intrumets onboard ENVISAT was used. Synergetic Aerosol Retrieval (SYNAER) observational and model (MATCH) data can be coupled by means of data assimilation. SYNAER measurements are able to distinguish between different aerosol components such as water-soluble, soot, sea salt and long-range transported mineral aerosols. During the assimilation procedure, the final analysis is highly dependent on the specification of the relative weights to both model and satellite source of information through the error covariance matrices. Since observation and background error covariance matrices are not perfectly known, a large potential for improvements of the analyses is offered by methods allowing their constructing and tuning. In this study, the method proposed by Desroziers and Ivanov (2001) is used to tune background and observational error statistics of the assimilation procedure.

 

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