Inversion of IASI observations in all-sky conditions

This thesis investigates the performance of the inversion code IOTA, based on the radiative transfer model sigma, under all-sky conditions using observations from the Infrared Atmospheric Sounding Interferometer (IASI). Retrievals in the presence of scattering particles like aerosols are particularly important, as operational algorithms still show limited capability under these conditions, especially in the infrared. IOTA relies on the Optimal Estimation approach and employs PCA state compression. Aerosol prior information is introduced through an ad-hoc covariance matrix that produces physically plausible vertical profiles while providing regularization for the inversion. First, the robustness of the inversion framework is assessed under clear-sky conditions. Then, a subset of aerosol-affected cases is analyzed by performing the inversion including dust parameters in the state vector. The results are evaluated through two approaches: temperature and humidity profiles are validated against radiosonde measurements and the internal consistency of the retrieval is assessed through statistical diagnostics. The retrieval results show good agreement with radiosonde measurements and a clear gain of information during the retrieval is observed. The aerosol retrieval experiments indicate a measurable sensitivity of both the instrument and the inversion algorithm to dust, allowing the retrieval of aerosol content and effective radius profiles. When aerosols are included in the retrieval for cases where they are detected, an improvement is observed both in the agreement with radiosonde measurements and in the statistical analysis compared to the clear-sky configuration. The results demonstrate that the IOTA inversion framework can handle atmospheric conditions including aerosols, and that its performance improves when a more realistic and complex description of the atmospheric state is incorporated into the retrieval process.