Yuxuan Wang

Satellite instruments have the most potential of capturing trace gas variability as they continually observe the atmosphere and its composition over wide regions. Yet the increasingly large data size of satellite products poses a challenge for their use as traditional data processing methods (e.g., averaging) may not be effective to extract the spatiotemporal variability without prior knowledge of an emission source's spatial and temporal behavior, such as location, time, and plume shape. Here, an agile clustering algorithm entitled CLustering of Atmospheric Satellite Products (CLASP) is presented to identify the spatiotemporal variability of trace gases captured in satellite observations. We find the knowledge discovery method for large data sets, clustering, is suited for identifying the variability of trace gases in satellite observations, as such CLASP is rooted in density‐based clustering methods. CLASP detects features from satellite observations and identifies their spatial, magnitude, and temporal axis leading to a better understanding of the spatiotemporal variability of atmospheric trace gases. To test the applicability of CLASP, the algorithm is applied to TROPOspheric Monitoring Instrument NO 2 observations illustrating some of its different capabilities. Implementing CLASP for event identification, capturing plume variability, and source detection, CLASP identified wildfires, observed disruptions from COVID‐19 lockdown restrictions, and detected irregular emissions from oil and gas operations.