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Workshop: AI for Earth and Space Science

Development and Statistical Analysis of an Automated Meteor Detection Pipeline for GOES Weather Satellites

Jeffrey Smith · Robert Morris · Randolph Longenbaugh · Alexandria Clark · Jessie Dotson · Nina McCurdy · Christopher Henze


The Geostationary Lightning Mapper (GLM) instrument onboard the GOES 16 and 17 weather satellites has been shown to be capable of detecting bolides (very bright meteors) in the Earth's atmosphere. Due to its large, continuous field of view and immediate public data availability, GLM provides a unique opportunity to detect a large variety of bolides, including those in the 0.1 to 3 m diameter range that complements current ground-based bolide detection systems, which are typically sensitive to smaller objects. Our goal is to generate a large catalog of calibrated bolide light curves to provide an unprecedented data set for three purposes: 1) to inform meteor entry models on how incoming bodies interact with the atmosphere, 2) to infer the pre-entry properties of the impacting bodies and 3) to statistically analyse bolide impact populations across the globe. We have deployed a machine learning based bolide detection and light curve generation pipeline on the NASA Advanced Supercomputer Facility. Detections are promptly published on a publicly available website, The pipeline has now been operational for almost 3 years and we have amassed a catalogue of over 3300 bolides. We first summarise the end-to-end development life cycle of the machine learning based bolide detection pipeline. We then present a statistical analysis of the bolides detected and assess the reliability of the automated detection pipeline.

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