A GEO‐LEO Imager Data Set for Characterizing Rapid Changes of Global Aerosol Properties

Abstract

Aerosols, the ubiquitous small particulates in our atmosphere, have climate, air quality, and/or other impacts far from their original sources. The Dark Target Algorithm (DTA), originally developed for retrieving aerosol properties from satellite imagers in Low‐Earth Orbit (LEO), has been ported to imagers in Geostationary (GEO) orbit. Using a consistent implementation of DTA, we created a new open‐source aerosol data set that provides optical depth (AOD) and other properties from a combination of 4 LEO and 4 GEO sensors for the period 2019–2022. This GEO‐LEO data set includes AOD from the individual sensors and aggregations at half‐hourly intervals on a quarter degree latitude/longitude grid. The sensor‐specific Level 2 retrievals are validated with respect to ground‐based sun photometers, and Level 3 statistics are diagnosed for residual dependencies on observing geometry and sampling versus time‐of‐day. The merged product yields near‐global snapshots, which help track rapidly evolving plumes on continental and inter‐hemispheric scales. Case studies for two events, the January 2020 Australian fires and August 2021 North American fires, show that GEO‐LEO captures the movement and magnitude of plumes crossing oceans or continents and that furthermore, these temporal snapshots help evaluate the timing and placement of plumes in model outputs. Additionally, we demonstrate that for a 2‐month period in 2019, data assimilation of these new products can improve a model's aerosol representation. Here, we provide details of the GEO‐LEO product now in the NASA archive, including content, format, and means of access. Finally, we describe known caveats regarding systematic biases and suggest some possible steps to improve.