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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. 

Abstract 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/s², but high‐deceleration events of up to 1,000 km/s²previously observed in head echo studies are not observed. 

Abstract We present results and analysis of finite‐difference time‐domain (FDTD) simulations of electromagnetic waves scattering off meteor head plasma using an analytical model and a simulation‐derived model of the head plasma distribution. The analytical model was developed by (Dimant & Oppenheim, 2017b,https://doi.org/10.1002/2017JA023963) and the simulation‐derived model is based on particle‐in‐cell (PIC) simulations presented in (Sugar et al., 2019,https://doi.org/10.1029/2018JA026434). Both of these head plasma distribution models show the meteor head plasma is significantly different than the spherically symmetric distributions used in previous studies of meteor head plasma. We use the FDTD simulation results to fit a power law model that relates the meteoroid ablation rate to the head echo radar cross section (RCS), and show that the RCS of plasma distributions derived from the Dimant‐Oppenheim analytical model and the PIC simulations agree to within 4 dBsm. The power law model yields more accurate meteoroid mass estimates than previous methods based on spherically symmetric plasma distributions. 

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. 

The diffusive tortuosity factor of a porous media quantifies the material’s resistance to diffusion, an important component of modeling flows in porous structures at the macroscale. Advances in X-ray micro-computed tomography (-CT) imaging provide the geometry of the material at the microscale (microstructure) thus enabling direct numerical simulation (DNS) of transport at the microscale. The data from these DNS are then used to close material’s macroscale transport models, which rely on effective material properties. In this work, we present numerical methods suitable for large scale simulations of diffusive transport through complex microstructures for the full range of Knudsen regimes. These numerical methods include a finite-volume method for continuum conditions, a random walk method for all regimes from continuum to rarefied, and the direct simulation Monte Carlo method. We show that for particle methods, the surface representation significantly affects the accuracy of the simulation for high Knudsen numbers, but not for continuum conditions. We discuss the upscaling of pore-resolved simulations to single species and multi-species volume-averaged models. Finally, diffusive tortuosities of a fibrous material are computed by applying the discussed numerical methods to 3D images of the actual microstructure obtained from X-ray computed micro-tomography.