Envisat Symposium Gothenburg

Advanced MIPAS-Level-2 Data Analysis (AMIL2DA)

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 and NO2, MIPAS infrared spectral limb emission measurements contain information on a bulk of further important species. 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. First, forward radiative transfer algorithms and retrieval processors are adapted to the physical and computational needs of the MIPAS experiment. These codes then are cross- validated by intercomparison to reveal potential weaknesses of the assessed computer models. Spectroscopic line data not included in the current databases but important to MIPAS applications are generated.

After 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. The different inversion algorithms are applied to a common set of synthetic measurement data in a blind-test validation 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. Furthermore, GOMOS, SCIAMACHY, and other data are used for validation.