Passive remote sensing of marine liquid cloud geometric thickness using the O2-O2 band: first results from TROPOMI Wenwu Wang1,2, Chong Shi1*, Jian Xu3, Shuai Yin1, Huazhe Shang1, Yutong Wang1,2, Chenqian Tang1, Ruijie Yao1,2, Guangyu Shi4, Husi Letu1*
1Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences. Beijing 100101, China.
2University of Chinese Academy of Sciences, Beijing 100049, China.
3National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China.
4State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China.
Abstract
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 analyse 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.
Keywords: hypespectral, cloud geometric thickness, O4 band
