Bolides are detected by the Geostationary Lightning Mapper onboard the GOES‐16 weather satellite, which takes images of Earth at a rate of 500 Hz in a 1.1 nm wide pass band centered on 777.4 nm wavelength. Ten case studies are discussed. These initial results were obtained using the Level 0 data received during the nonoperational in‐orbit postlaunch test period. GLM positions and timings are sufficiently accurate to assist in trajectory and orbit reconstruction. GLM samples the light curve nearly completely, unaffected by onboard and downlink processes tailored to lightning data. Sufficient data on the instantaneous background scene are provided to reconstruct the baseline drift in the brightest pixels. The agreement to within a factor of 2–3 between measured total radiated energy from GLM and that derived from other space‐borne observations implies that during the bolide's peak brightness the GLM pass band is dominated by continuum emission, rather than O I line emission. The reported flux is corrected for angle‐from‐nadir shifts in the central wavelength of the pass band, which overestimates continuum flux by only up to 20% for most of the GLM field of view, but more so if the bolide is observed far from nadir. Assuming a 6000 K blackbody spectrum, GLM is able to detect bolides with peak visual magnitude (at a normalized 100 km distance) brighter than about −14 in nighttime, and slightly brighter in daytime.

The Geostationary Lightning Mapper (GLM) instrument onboard the GOES 16 and 17 satellites can be used to detect bolides in the atmosphere. This capacity is unique because GLM provides semi-global, continuous coverage and releases its measurements publicly. Here, six filters are developed that are aggregated into an automatic algorithm to extract bolide signatures from the GLM level 2 data product. The filters exploit unique bolide characteristics to distinguish bolide signatures from lightning and other noise. Typical lightning and bolide signatures are introduced and the filter functions are presented. The filter performance is assessed on 144845 GLM L2 files (equivalent to 34 days-worth of data) and the algorithm selected 2252 filtered files (corresponding to a pass rate of 1.44%) with bolide-similar signatures. The challenge of identifying frequent but small, decimeter-sized bolide signatures is discussed as GLM reaches its resolution limit for these meteors. The effectiveness of the algorithm is demonstrated by its ability to extract confirmed and new bolide discoveries. We provide discovery numbers for November 2018 when seven likely bolides were discovered of which four are confirmed by secondary observations. The Cuban meteor on Feb 1st 2019 serves as an additional example to demonstrate the algorithms capability and the first light curve as well as correct ground track was available within 8.5 hours based on GLM data for this event. The combination of the automatic bolide extraction algorithm with GLM can provide a wealth of new measurements of bolides in Earth’s atmosphere to enhance the study of asteroids and meteors.

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Prepared by Yucca Mountain Site Characterization Project (YMSCP) participants as part of the Civilian Radioactive Waste Management Program (CRWM). The YMSCP is managed by the Yucca Mountain Project Office of the U.S. Department of Energy, DOE Field Office, Nevada (DOE/NV). YMSCP work is sponsored by the Office of Geologic Repositories (OGR) of the DOE office of Civilian Radioactive Waste Management (OCRWM)." Issued by Sandia National Laboratories, operated for the United States Department of Energy by Sandia Corporation. NOTICE: This report was prepared as an account of work sponsored by a n agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, nor any of their contractors, subcontractors, or their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government, any agency thereof or any of their contractors or subcontractors. The views and opinions expressed herein do not necessarily state or reflect those of the United States Government, any agency thereof or any of their contractors. Printed in the United States of America. This report has been reproduced directly from the best available copy.

Among the background signals commonly seen by Earth-monitoring satellites is the specular reflection of sunlight off of Earth's surface, commonly referred to as a glint. This phenomenon, involving liquid or ice surfaces, can result in the brief, intense illumination of satellite sensors appearing from the satellite perspective to be of terrestrial origin. These glints are important background signals to be able to identify with confidence, particularly in the context of analyzing data from satellites monitoring for transient surface or atmospheric events. Here we describe methods for identifying glints based on the physical processes involved in their production, including spectral fitting and polarization measurements. We then describe a tool that, using the WGS84 spheroidal Earth model, finds the latitude and longitude on Earth where a reflection of this type could be produced, given input Sun and satellite coordinates. This tool enables the user to determine if the surface at the solution latitude and longitude is in fact reflective, thus identifying the sensor response as a true glint or an event requiring further analysis. 4 5