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Changes in atmospheric composition are important in the context of stratospheric ozone depletion, global change and related environmental problems. The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), which is a core instrument of the Envisat polar platform to be launched in 2001 by the European Space Agency (ESA), is a powerful tool to measure vertical profiles of trace species on a global scale. While operational data processing by ESA covers only analysis of pressure, temperature, and the mixing ratios of the species O3, H2O, HNO3, CH4, and N2O, MIPAS infrared spectral limb emission measurements contain information on a bulk of further species relevant to environmental problems mentioned above. The goal of AMIL2DA is to generate data analysis tools for these supplemental species along with thorough validation of these algorithms.
Instead of merging the contributions of all participants to one data analysis algorithm which fits all purposes, the AMIL2DA strategy is to maintain the diversity of different computer codes by each group which are custom-tailored to their specific scientific needs. Generally, data analysis consists of forward modeling of radiance spectra and inversion of measurement data. As a first step, forward radiative transfer algorithms and retrieval processors are adapted to the physical and computational needs of the MIPAS experiment. This includes adaptation to high resolution limb emission measurements, acceleration of numerical methods, and automated provision of input data as well as generation of spectroscopic line data not included in the current databases but presumably important to MIPAS applications. In a second step, these codes are cross-validated by a blind-test intercomparison which is supposed to reveal potential weaknesses of assessed computer models. In particular the relevance of breakdown of thermodynamic equilibrium in the atmosphere is emphasized. After successful cross-validation of forward radiative transfer models and subsequent upgrading, these are operated in the context of an inversion computer code, which infers atmospheric constituent abundances from measured spectra. For purpose of cross-validation, different inversion algorithms are applied to a common set of synthetic measurement data in a blind-test mode. After upgrading the inversion models and fine-tuning of processing parameters, a common agreed set of real MIPAS measurements is used for further testing. Residuals between measured and best-fitting modeled spectra are analyzed for systematic patterns. Emphasis is put on candidate explanations such as inappropriate predictions on instrument characteristics; different use of initial guess and a priori data; over- or under-regularization of the retrieval, and possible deficiencies in spectroscopic data. As an external reference, GOMOS, SCIAMACHY, and other data are used for validation purposes. The advantage of GOMOS and SCIAMACHY is that non-local thermodynamic equilibrium is not an issue.
These activities are the basis to better exploit existing MIPAS data by inferring vertical profiles of species relevant to ozone destruction and global change. Deficiencies in forward radiative transfer as well as inversion algorithms are detected and removed, and confidence in retrieval strategies and data products is strengthened. Completeness and appropriateness of physical effects included in the involved radiative transfer models is proved. Standardization of data products is gained while the diversity of data analysis strategies used by different European groups is to be maintained. The basis for a sound scientific analysis of MIPAS data is provided.