Abstract. Meteor radars have become widely used instruments to study atmospheric dynamics, particularly in the 70 to 110 km altitude region. Thesesystems have been proven to provide reliable and continuous measurements of horizontal winds in the mesosphere and lower thermosphere. Recently,there have been many attempts to utilize specular and/or transverse scatter meteor measurements to estimate vertical winds and vertical windvariability. In this study we investigate potential biases in vertical wind estimation that are intrinsic to the meteor radar observation geometryand scattering mechanism, and we introduce a mathematical debiasing process to mitigate them. This process makes use of a spatiotemporal Laplacefilter, which is based on a generalized Tikhonov regularization. Vertical winds obtained from this retrieval algorithm are compared to UA-ICON modeldata. This comparison reveals good agreement in the statistical moments of the vertical velocity distributions. Furthermore, we present the firstobservational indications of a forward scatter wind bias. It appears to be caused by the scattering center's apparent motion along the meteortrajectory when the meteoric plasma column is drifted by the wind. The hypothesis is tested by a radiant mapping of two meteor showers. Finally, weintroduce a new retrieval algorithm providing a physically and mathematically sound solution to derive vertical winds and wind variability frommultistatic meteor radar networks such as the Nordic Meteor Radar Cluster (NORDIC) and the Chilean Observation Network De meteOr Radars(CONDOR). The new retrieval is called 3DVAR+DIV and includes additional diagnostics such as the horizontal divergence and relative vorticity toensure a physically consistent solution for all 3D winds in spatially resolved domains. Based on this new algorithm we obtained vertical velocitiesin the range of w = ± 1–2 m s−1 for most of the analyzed data during 2 years of collection, which is consistent with the values reportedfrom general circulation models (GCMs) for this timescale and spatial resolution.
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Meteoroid orbit determination from HPLA radar data
High-power large-aperture radars have revolutionized meteor science by allowing highly accurate position and velocity estimates to be made from meteor head echoes. This paper describes a new open-source software, MODA, for determining the heliocentric orbital parameters of these meteoroids. We compare MODA with other current methods, both analytical and numerical. We describe our modeling of third-body perturbations and atmospheric drag, as well as solar radiation pressure, which is not taken into account in other works. We verify MODA against results from the literature and use it to compute the orbits for two small particles observed by ALTAIR in 2008.
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- PAR ID:
- 10475930
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Icarus
- Volume:
- 386
- Issue:
- C
- ISSN:
- 0019-1035
- Page Range / eLocation ID:
- 115144
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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We present a machine-learning approach to detect and analyze meteor echoes (MADAME), which is a radar data processing workflow featuring advanced machine-learning techniques using both supervised and unsupervised learning. Our results demonstrate that YOLOv4, a convolutional neural network (CNN)-based one-stage object detection model, performs remarkably well in detecting and identifying meteor head and trail echoes within processed radar signals. The detector can identify more than 80 echoes per minute in the testing data obtained from the Jicamarca high power large aperture (HPLA) radar. MADAME is also capable of autonomously processing data in an interferometer mode, as well as determining the target’s radiant source and vector velocity. In the testing data, the Eta Aquarids meteor shower could be clearly identified from the meteor radiant source distribution analyzed automatically by MADAME, thereby demonstrating the proposed algorithm’s functionality. In addition, MADAME found that about 50 percent of the meteors were traveling in inclined and near-inclined circular orbits. Furthermore, meteor head echoes with a trail are more likely to originate from shower meteor sources. Our results highlight the capability of advanced machine-learning techniques in radar signal processing, providing an efficient and powerful tool to facilitate future and new meteor research.
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Abstract A novel computer vision‐based meteor head echo detection algorithm is developed to study meteor fluxes and their physical properties, including initial range, range coverage, and radial velocity. The proposed Algorithm for Head Echo Automatic Detection (AHEAD) comprises a feature extraction function and a Convolutional Neural Network (CNN). The former is tailored to identify meteor head echoes, and then a CNN is employed to remove false alarms. In the testing of meteor data collected with the Jicamarca 50 MHz incoherent scatter radar, the new algorithm detects over 180 meteors per minute at dawn, which is 2 to 10 times more sensitive than prior manual or algorithmic approaches, with a false alarm rate less than 1 percent. The present work lays the foundation of developing a fully automatic AI‐meteor package that detects, analyzes, and distinguishes among many types of meteor echoes. Furthermore, although initially evaluated for meteor data collected with the Jicamarca VHF incoherent radar, the new algorithm is generic enough that can be applied to other facilities with minor modifications. The CNN removes up to 98 percent of false alarms according to the testing set. We also present and discuss the physical characteristics of meteors detected with AHEAD, including flux rate, initial range, line of sight velocity, Signal‐to‐Noise Ratio, and noise characteristics. Our results indicate that stronger meteor echoes are detected at a slightly lower altitude and lower radial velocity than other meteors.
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Abstract Both high‐power large aperture radars and smaller meteor radars readily observe the dense head plasma produced as a meteoroid ablates. However, determining the mass of such meteors based on the information returned by the radar is challenging. We present a new method for deriving meteor masses from single‐frequency radar measurements, using a physics‐based plasma model and finite‐difference time‐domain (FDTD) simulations. The head plasma model derived in Dimant and Oppenheim (2017),
https://doi.org/10.1002/2017ja023963 depends on the meteoroids altitude, speed, and size. We use FDTD simulations of a radar pulse interacting with such head plasmas to determine the radar cross section (RCS) that a radar system would observe for a meteor with a given set of physical properties. By performing simulations over the observed parameter space, we construct tables relating meteor size, velocity, and altitude to RCS. We then use these tables to map a set of observations from the MAARSY radar (53.5 MHz) to fully defined plasma distributions, from which masses are calculated. To validate these results, we repeat the analysis using observations of the same meteors by the EISCAT radar (929 MHz). The resulting masses are strongly linearly correlated; however, the masses derived from EISCAT measurements are on average 1.33 times larger than those derived from MAARSY measurements. Since this method does not require dual‐frequency measurements for mass determination, only validation, it can be applied in the future to observations made by many single‐frequency radar systems. -
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Abstract We conducted an all‐sky imaging transient search with the Owens Valley Radio Observatory Long Wavelength Array (OVRO‐LWA) data collected during the Perseid meteor shower in 2018. The data collection during the meteor shower was motivated to conduct a search for intrinsic radio emission from meteors below 60 MHz known as the meteor radio afterglows (MRAs). The data collected were calibrated and imaged using the core array to obtain lower angular resolution images of the sky. These images were input to a pre‐existing LWA transient search pipeline to search for MRAs as well as cosmic radio transients. This search detected 5 MRAs and did not find any cosmic transients. We further conducted peeling of bright sources, near‐field correction, visibility differencing and higher angular resolution imaging using the full array for these 5 MRAs. These higher angular resolution images were used to study their plasma emission structures and monitor their evolution as a function of frequency and time. With higher angular resolution imaging, we resolved the radio emission size scales to less than 1 km physical size at 100 km heights. The spectral index mapping of one of the long duration event showed signs of diffusion of plasma within the meteor trails. The unpolarized emission from the resolved radio components suggest resonant transition radiation as the possible radiation mechanism of MRAs.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.icarus.2022.115144
