Publications

Yang, CK; Chiu, JC; Marshak, A; Feingold, G; Varnai, T; Wen, GY; Yamaguchi, T; van Leeuwen, PJ (2022). Near-Cloud Aerosol Retrieval Using Machine Learning Techniques, and Implied Direct Radiative Effects. GEOPHYSICAL RESEARCH LETTERS, 49(20), e2022GL098274.

Abstract
There is a lack of satellite-based aerosol retrievals in the vicinity of low-topped clouds, mainly because reflectance from aerosols is overwhelmed by three-dimensional cloud radiative effects. To account for cloud radiative effects on reflectance observations, we develop a Convolutional Neural Network and retrieve aerosol optical depth (AOD) with 100-500 m horizontal resolution for all cloud-free regions regardless of their distances to clouds. The retrieval uncertainty is 0.01 + 5%AOD, and the mean bias is approximately -2%. In an application to satellite observations, aerosol hygroscopic growth due to humidification near clouds enhances AOD by 100% in regions within 1 km of cloud edges. The humidification effect leads to an overall 55% increase in the clear-sky aerosol direct radiative effect. Although this increase is based on a case study, it highlights the importance of aerosol retrievals in near-cloud regions, and the need to incorporate the humidification effect in radiative forcing estimates.

DOI:
10.1029/2022GL098274

ISSN:
1944-8007