Identification of polluted clouds and composition analysis based on GF-5 DPC data

Abstract Large amounts of fine polluted particles due to anthropogenic activities can enter into the lower atmosphere and may pollute the lower water clouds, especially continental cumulus. Continental polluted cumulus (CPCU) plays an important role in the process of atmospheric radiative transfer and the Earth's environmental change. Based on the sensitivity of polarization to small particles, GF-5 DPC data were used to identify polluted clouds. In addition, dynamic transport and composition of polluted clouds were further analyzed. Firstly, a novel method based on two tests was used to identify polluted clouds in China. The source and transport characteristics of the polluted clouds were analyzed by using wind field and backward trajectory pattern. Secondly, based on the monitoring data from the Sun‐sky radiometer Observation NETwork (SONET), the types of atmospheric aerosol composition in China were classified into ten types by considering influences of the climate types, Digital Elevation Model and social economy, etc. Finally, the composition of polluted clouds in different regions was further identified according to regional aerosol composition, and the results were verified by the Optical Properties of Aerosols and Clouds (OPAC) software. The identifying results of polluted clouds showed that there were four polluted clouds over China on May 27, 2018 and high concentration of small dust particles. The changes in the component ratios of different polluted clouds respond well to the results of airflow sources and transport. Compared with the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Vertical Feature Mask (VFM) data, the proposed method is effective and robust. The proposed method of identification and composition analysis based on GF-5 DPC data for polluted clouds can enrich the application of DPC data in the field of atmospheric and environmental detection.