Resolving the ambiguous direction of arrival of weak meteor radar trail echoes - Kastinen, Daniel; Kero, Johan; Kozlovsky, Alexander; Lester, Mark (Atmospheric Measurement Techniques, 2021)
Meteor phenomena cause ionized plasmas that can be roughly divided into two distinctly different regimes: a dense and transient plasma region co-moving with the ablating meteoroid and a trail of diffusing plasma left in the atmosphere and moving with the neutral wind. Interferometric radar systems are used to observe the meteor trails and determine their positions and drift velocities. Depending on the spatial configuration of the receiving antennas and their individual gain patterns, the voltage response can be the same for several different plane wave directions of arrival (DOAs), thereby making it impossible to determine the correct direction. A low signal-to-noise ratio (SNR) can create the same effect probabilistically even if the system contains no theoretical ambiguities. Such is the case for the standard meteor trail echo data products of the Sodankylä Geophysical Observatory SKiYMET all-sky interferometric meteor radar. Meteor trails drift slowly enough in the atmosphere and allow for temporal integration, while meteor head echo targets move too fast. Temporal integration is a common method to increase the SNR of radar signals. For meteor head echoes, we instead propose to use direct Monte Carlo (DMC) simulations to validate DOA measurements. We have implemented two separate temporal integration methods and applied them to 2222 events measured by the Sodankylä meteor radar to simultaneously test the usefulness of such DMC simulations on cases where temporal integration is possible, validate the temporal integration methods, and resolve the ambiguous SKiYMET data products. The two methods are the temporal integration of the signal spatial correlations and matched-filter integration of the individual radar channel signals. The results are compared to Bayesian inference using the DMC simulations and the standard SkiYMET data products. In the examined data set, ∼ 13 % of the events were indicated as ambiguous. Out of these, ∼ 13 % contained anomalous signals. In ∼ 95 % of all ambiguous cases with a nominal ...