Yang, C. KevinChiu, ChristineMarshak, AlexanderFeingold, GrahamVárnai, TamásWen, GuoyongYamaguchi, Takanobuvan Leeuwen, Peter Jan2022-09-192022-09-192022https://hdl.handle.net/10217/235755http://dx.doi.org/10.25675/10217/235755The dataset contains two parts: 1) Large eddy simulation (LES) output 2) a module that performs all-clear-sky aerosol retrieval using a machine-learning-based model. There are also example data and a script to demonstrate how to use the module.Department of Atmospheric ScienceThere 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.TXTMATLABHDF5PYNetCDFengPython code is distributed under an MIT License (see LICENSE.txt file).Aerosol direct radiative effectAerosol remote sensingMachine LearningDatasetData are distributed under the terms and conditions of the Creative Commons CC BY: Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/).