Retrieval of atmospheric and cloud profiles from high-spectral resolution infrared observations

This study presents an optimal estimation retrieval framework for deriving atmospheric thermodynamic state and cloud microphysical properties from infrared radiance measurements. The proposed method exploits a fast radiative transfer model, σ, capable of computing atmospheric emission spectra in all-sky conditions. The retrieval framework follows the optimal estimation approach, enhanced by principal component analysis state compression. Cloud prior information is introduced through a dedicated covariance matrix designed to generate physically plausible vertical profiles of ice content and effective dimensions and stabilize the inversion. This combination of methods offers a robust and flexible solution for retrieving atmospheric and cloud parameters from spectrally resolved infrared observations. The retrieval algorithm is tested on both synthetic and real Infrared Atmospheric Sounding Interferometer (IASI) acquisitions. The results highlight the capability of the presented scheme in retrieving cloud property profiles, such as optical depth, effective dimension, and cloud position and its feasible application to operational processors.