Abstract
Publicly accessible weather radar data have significant capabilities for meteorological measurements and predictions and, further, have the potential to measure nonmeteorological events that include smoke, ash, and debris plumes as well as explosions. The ability to identify and track nonmeteorological events can be of assistance in emergency response, hazard mitigation, and related activities in locations where radar coverage both exists and is recorded and accessible to the user. In this study, events from multiple locations in the United States that are reported in news outlets are assessed using a manual inspection process of Level 2 weather radar data to identify anthropogenic and nonbiological returns. Explosive events are also identified, and a large high-altitude debris cloud from the intentional destruction of the SpaceX Starship is tracked across a wide area. Finally, future efforts using a machine learning model are discussed as a means of automating the process and potentially enabling near-real-time nonmeteorological event identification in the same areas where the data are accessible. Using weather radar data can be a valuable new tool for Department of Defense systems to aid in military awareness, and for interagency emergency response and forensic mission experts to consider national weather service data in their mission profiles. Radar data can be effective in detecting several common types of emergencies and inform and aid response personnel.
Original language | English |
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Pages (from-to) | 351-367 |
Number of pages | 17 |
Journal | Journal of Emergency Management |
Volume | 21 |
Issue number | 4 |
DOIs | |
State | Published - Jul 2024 |
Funding
This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains, and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (https://www.energy. gov/doe-public-access-plan). Furthermore, the support of Dr. Jason Hite (Oak Ridge National Laboratory) for the machine learning implementation is gratefully acknowledged. The work was funded by the Defense Threat Reduction Agency under DOE agreement number 0000-Z341-19.
Funders | Funder number |
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U.S. Department of Energy | |
Oak Ridge National Laboratory | |
DOE Public Access Plan | |
Defense Threat Reduction Agency | 0000-Z341-19 |
Keywords
- emergency
- explosions
- hazard
- radar
- weather