Water Vapor Content Retrieval Under Cloudy Sky Conditions From SWIR Satellite Measurements in the Context of C³IEL Space Mission Project

Abstract. A retrieval algorithm of integrated water vapor content above cloud, using shortwave infrared observations, is developed and evaluated through idealized and realistic atmospheric profiles. Water vapor plays a crucial role in cloud formation and development, particularly those resulting from convective processes. They influence locally the spatiotemporal variability of atmospheric water vapor content, through exchanges between clouds and their immediate environment. Therefore, a better understanding of water vapor content above and around cloud is necessary to improve our comprehension of interactions between water vapor and cloud to better constrain Large-Eddy Simulation and numerical weather forecasting models. The developed algorithm is part of the Cluster for Cloud evolution, ClImatE and Lightning, C³IEL space mission project. This mission, scheduled for 2027 aims to enhance our knowledge of the 3D convective cloud development velocities, the electrical activity associated with convective systems and the water vapor content above and around cloud. The retrieval algorithm presented in this study was achieved through a Bayesian probabilistic approach, the optimal estimation method. The atmosphere was assumed to be composed of homogeneous plane-parallel layers, and synthetic radiance datasets were generated for testing the developed retrieval algorithm. The feasibility of retrieving integrated water vapor content above cloud and over the ocean from SWIR radiances was shown to have a precision of approximately 1 kg.m^-2 for optically thick clouds under idealized cloudy sky conditions. Tests using realistic water vapor and cloud extinction profiles that present non-homogeneous vertical distributions show that integrated water vapor content above low/mid-level clouds could be retrieved with a positive bias related to cloud vertical penetration of approximately 2.6 kg.m^-2. The algorithm fails for very low water vapor content encountered in the presence of high deep convective clouds.