Calculating meteoroid masses from photometric observations relies on prior knowledge of the luminous efficiency, a parameter that is not well characterized; reported values vary by several orders of magnitude. We present results from an experimental campaign to determine the luminous efficiency as a function of mass, velocity, and composition. Using a linear electrostatic dust accelerator, iron and aluminum microparticles were accelerated to 10+ km/s and ablated, and the light production measured. The luminous efficiency of each event was calculated and functional forms fit for each species. For both materials, the luminous efficiency is lowest at low velocities, rises sharply, then falls as velocity increases. However, the exact shape and magnitude of the curve is not consistent between the materials. The difference between the luminous efficiencies for iron and aluminum, particularly at high velocities, indicates that it is not sufficient to use the same luminous efficiency for all compositions and velocities.

Radars detect plasma trails created by the billions of small meteors that impact the Earth’s atmosphere daily, returning data used to infer characteristics of the meteoroid population and upper atmosphere. Researchers use models to investigate the dynamic evolution of the trails, enabling them to better interpret radar results. This paper presents a fully kinetic, 3D code to explore the impacts of three trail characteristics: length, neutral wind speed, and ablation altitude. The simulations characterize the turbulence that develops as the trail evolves and these are compared to radar data. They also show that neutral winds drive the formation of waves and turbulence in trails, and that wave amplitudes increase with neutral wind speed. The finite trail simulations demonstrate that the bulk motion of the trail flows with the neutral wind. A detailed analysis of simulated trail spectra yield spectral widths, and evaluate signal strength as a function of aspect angle. Waves propagate primarily along the length of the trail in all cases, and most power is in modes perpendicular to $\mathit{\vec{B}}$. Persistent waves develop at wavelengths corresponding to the gradient scale length of the original trail. Our results show that the rate at which power drops with respect to aspect angle in meter-scale modes increases from $5.7$ dB/degree to $6.9$ dB/degree with a 15 km increase in altitude. The results will allow researchers to draw more detailed and accurate information from non-specular radar observations of meteors.

Both high-power large aperture (HPLA) 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~\citeA{dimopp17} 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.