Passive Remote Sensing of Marine Liquid Cloud Geometric Thickness Using the O2–O2 Band: First Results From TROPOMI

Observations on cloud geometric thickness are crucial for understanding the radiative balance and aerosol indirect radiative effects, and currently, cloud geometric thickness retrieval studies for passive instruments remain constrained due to the lack of the understanding of the incident radiation penetrability. In this work, we firstly analyze the relationship between the cloud droplets distribution and the incident radiation penetrability based on physical model, and then fully utilize the advantages of hyperspectral O₄ measurements to build a physically based machine learning model to retrieve the cloud geometric thickness. The algorithm retrieves cloud geometric thickness from TROPOMI observations for the first time, and the retrievals are compared with the cloud geometric thickness from active observations. It is found that the mean absolute error of the retrievals using 2B-CLDPROF-LIDAR cloud-top height as input is 0.49 km, which shows the potential of O₄ band to retrieve cloud geometric thickness.