Bian, QijingKreidenweis, SoniaChiu, J. ChristineMiller, Steven D.Xu, XiaoguangWang, JunKahn, Ralph A.Limbacher, James A.Remer, Lorraine A.Levy, Robert C.2021-10-132021-10-132021https://hdl.handle.net/10217/233960http://dx.doi.org/10.25675/10217/233960The dataset includes the data for figures shown in the paper. Two opposing GEOS satellites capture a dust event in the Gulf of Mexico in June 2019. We demonstrate a first-guess phase function to reconstruct dust phase function by leveraging GEOS satellites. We then evaluate our methodology using a different dust event over the Gulf of Mexico in June 2020.Department of Atmospheric ScienceCooperative Institute for Research in the AtmospherePassive satellite observations play an important role in monitoring global aerosol properties and helping quantify aerosol radiative forcing in the climate system. The quality of aerosol retrievals from the satellite platform relies on well-calibrated radiance measurements from multiple spectral bands, and the availability of appropriate particle optical models. Inaccurate scattering phase function assumptions can introduce large retrieval errors. High-spatial resolution, dual-view observations from the Advanced Baseline Imagers (ABI) on board the two most recent Geostationary Operational Environmental Satellites (GOES), East and West, provide a unique opportunity to better constrain the aerosol phase function. Using dual GOES reflectance measurements for a dust event in the Gulf of Mexico in 2019, we demonstrate how a first-guess phase function can be reconstructed by considering the variations in observed scattering angle throughout the day. Using the reconstructed phase function, aerosol optical depth retrievals from the two satellites are self-consistent and agree well with surface-based optical depth estimates. We evaluate our methodology and reconstructed phase function against independent retrievals made from low-Earth-orbit multi-angle observations for a different dust event in 2020. Our new aerosol optical depth retrievals have a root-mean-square-difference of 0.019– 0.047. Furthermore, the retrievals between the two geostationary satellites for this case agree within about 0.059±0.072, as compared to larger discrepancies between the operational GOES products at times, which do not employ the dual-view technique.ZIPCSVHDF5engGEOSdust modelphase functionDatasetThis material is open access and distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).