High spatio-temporal resolution aerosol monitoring is critical to understand and mitigate air pollution and climate change. In this context, geostationary satellite instruments can be extremely beneficial, allowing fine-grained temporal characterization of aerosols over large regions. In this study, we combine data from the geostationary instruments Advanced Baseline Imager (ABI) on-board GOES-East and GOES-West, using Deep Learning methods to post-process NASA Dark Target ABI AOD and NOAA ABI AOD products and improve their accuracy and spatial resolution. We deploy a Transformer Encoder architecture, and compare it to a Multi Layer Perceptron (MLP) architecture predicting at single time step, showing how exploiting the temporal patterns in geostationary daily observations leads to improved accuracy and generalization in the post-process correction. Additionally, we show that further improvement can be obtained combining multi-view angles from different (though very similar) geostationary satellites. Our region of interest is the Contiguous United States (CONUS) in the years 2020–2022.