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).https://creativecommons.org/licenses/by/4.0/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/).