Aerosol Detection product with GOES-R Advanced Baseline Imager (ABI) measurements

Episodic events such as smoke and dust outbreaks impact human health and economy. Therefore, it is desirable to have qualitative but fast information on the time, location and coverage of these outbreaks for air quality monitoring. GOES-R ABI is designed to observe the Americas in a 5-minute interval and at 0.5, 1, 2 km spatial resolution at visible, near-IR, and IR channels respectively. Taking advantage of the unique capability of GOES-R ABI, i.e., high temporal and spatial resolution, and multi-spectral observations, an algorithm was designed to identify pixels which are loaded with either smoke or dust. Aerosol Detection algorithm utilizes the fact that smoke/dust exhibit features of spectral dependence and contrast over both visible and infrared spectrum that are different from clouds, surface, and clear-sky atmosphere. The fundamental principle of the detection algorithm depends on threshold tests which separate smoke/dust from cloud and clear-sky over water and land. In this paper, we present detailed description on the algorithm and validation of the smoke/dust detection product. Under current pre-lunch stage, MODIS observations are being used as proxy and the results of the smoke/dust algorithm are shown for different scenarios such as prescribed agricultural burning, wild fires, dust storms, and dust transport from Africa. The performance of the algorithm was evaluated by comparing to MODIS RGB images and other satellite products such as CALIPSO. It is shown that the requirement of 80% correct detection for dust over water and land, for smoke over land, and 70% correct detection for smoke over water, can be achieved.