Latest developments and upcoming innovations in Py4CAtS---Python for Computational Atmospheric Spectroscopy

Abstract

We present the latest version of Py4CAtS---Python for Computational Atmospheric Spectroscopy. Py4CAtS has expanded its capabilities to facilitate the retrieval of gases from SWIR (Short-Wave InfraRed) nadir observations and temperature fits from TIR (Thermal InfraRed) spectra. It also supports spherical geometry, which is crucial for accurate modeling of observations at high zenith angles. We are developing tools that will allow users to choose from different inverse solvers to infer various geophysical parameters from a variety of space borne and airborne sensors, including TROPOMI (TROPOspheric Monitoring Instrument). Initial results are shown, which demonstrate the new capabilities. We have also developed a new two-grid algorithm for line-by-line computation that enables line wing cuts mandatory for the MT-CKD (Mlawer-Tobin-Clough-Kneizys-Davies) water continuum model. The inclusion of Collision-Induced Absorption (CIA) and the MT-CKD water continua significantly improve our model's ability to simulate observed spectra with high accuracy. Moreover, we also highlight the importance of higher-order line shapes such as the speed-dependent Rautian and Hartmann-Tran profiles.