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On 10 and 11 October 2019, high‐power radar observations were performed simultaneously for 8 hours at Resolute Bay Incoherent Scatter North (RISR‐N), Jicamarca Radio Observatory (JRO), and Millstone Hill Observatory (MHO). The concurrent observations eliminate diurnal, seasonal, and space weather biases in the meteor head echo populations and elucidate relative sensitivities of each facility and configuration. Each facility observed thousands of head echoes, with JRO observing tens of thousands. An inter‐pulse phase matching technique employs Doppler shifts to determine head echo range rates (velocity component along radar beam) with order‐of‐magnitude greater accuracy versus measuring the Doppler shift at individual pulses, and this technique yields accurate range rates and decelerations for a subset of the head echo population at each facility. Because RISR‐N is at high latitude and points away from the ecliptic plane, it does not observe head echoes with range rates faster than 55 km/s, although its head echo population demonstrates a bias toward larger and faster head echoes. At JRO near the equator, a larger spread of range rates is observed. MHO observes a large spread of range rates at mid‐latitude despite its comparable frequency to RISR‐N, but this occurs because its beam was pointed at a 45° elevation angle unlike RISR‐N and JRO which were pointed near‐zenith. A trend of greater decelerations at lower altitudes is observed at RISR‐N and JRO, with decelerations of up to 60 km/s^2, but high‐deceleration events of up to 1,000 km/s^2 previously observed in head echo studies are not observed.
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Meteor Head Echo Detection at Multiple High‐Power Large‐Aperture Radar Facilities via a Convolutional Neural Network Trained on Synthetic Radar Data
Abstract High‐power large‐aperture radar instruments are capable of detecting thousands of meteor head echoes within hours of observation, and manually identifying every head echo is prohibitively time‐consuming. Previous work has demonstrated that convolutional neural networks (CNNs) accurately detect head echoes, but training a CNN requires thousands of head echo examples manually identified at the same facility and with similar experiment parameters. Since pre‐labeled data is often unavailable, a method is developed to simulate head echo observations at any given frequency and pulse code. Real instances of radar clutter, noise, or ionospheric phenomena such as the equatorial electrojet are additively combined with synthetic head echo examples. This enables the CNN to differentiate between head echoes and other phenomena. CNNs are trained using tens of thousands of simulated head echoes at each of three radar facilities, where concurrent meteor observations were performed in October 2019. Each CNN is tested on a subset of actual data containing hundreds of head echoes, and demonstrates greater than 97% classification accuracy at each facility. The CNNs are capable of identifying a comprehensive set of head echoes, with over 70% sensitivity at all three facilities, including when the equatorial electrojet is present. The CNN demonstrates greater sensitivity to head echoes with higher signal strength, but still detects more than half of head echoes with maximum signal strength below 20 dB that would likely be missed during manual detection. These results demonstrate the ability of the synthetic data approach to train a machine learning algorithm to detect head echoes.
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- PAR ID:
- 10519092
- Publisher / Repository:
- AGU
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Space Physics
- Volume:
- 129
- Issue:
- 4
- ISSN:
- 2169-9380
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1029/2023JA032204
