| Summary: | Measurements of the Earth's atmosphere are crucial for understanding the behavior of the atmosphere and the underlying chemical and dynamical processes. Adequate monitoring of stratospheric ozone and greenhouse gases, for example, requires continuous global observations. Although expensive to build and complicated to operate, satellite instruments provide the best means for the global monitoring. Satellite data are often supplemented by ground-based measurements, which have limited coverage but typically provide more accurate data. Many atmospheric processes are altitude-dependent. Hence, the most useful atmospheric measurements provide information about the vertical distribution of the trace gases. Satellite instruments that observe Earth's limb are especially suitable for measuring atmospheric profiles. Satellite instruments looking down from the orbit, and remote sensing instruments looking up from the ground, generally provide considerably less information about the vertical distribution. Remote sensing measurements are indirect. The instruments observe electromagnetic radiation, but it is ozone, for example, that we are interested in. Interpreting the measured data requires a forward model that contains physical laws governing the measurement. Furthermore, to infer meaningful information from the data, we have to solve the corresponding inverse problem. Atmospheric inverse problems are typically nonlinear and ill-posed, requiring numerical treatment and prior assumptions. In this work, we developed inversion methods for the retrieval of atmospheric profiles. We used measurements by Optical Spectrograph and InfraRed Imager System (OSIRIS) on board the Odin satellite, Global Ozone Monitoring by Occultation of Stars (GOMOS) on board the Envisat satellite, and ground-based Fourier transform spectrometer (FTS) at Sodankylä, Finland. For OSIRIS and GOMOS, we developed an onion peeling inversion method and retrieved ozone, aerosol, and neutral air profiles. From the OSIRIS data, we also retrieved NO2 profiles. For ...
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