Precision measurements of radar transverse scattering speeds from meteor phase characteristics

TL;DR

This paper introduces an improved radar technique, PSSST, for accurately measuring meteor speeds from phase data, demonstrating high precision and robustness across multiple radar systems and conditions.

Contribution

The paper presents the PSSST method, a novel sliding-slopes technique that enhances speed measurement accuracy from meteor radar echoes using phase data.

Findings

Achieves ±5% speed precision for speeds below 40 km/s.

Over 90% of CMOR echoes have successful PSSST solutions.

Robust against phase unwrapping errors and phase value selection.

Abstract

We describe an improved technique for using the backscattered phase from meteor radar echo measurements just prior to the specular point ($t_{0}$) to calculate meteor speeds and their uncertainty. Our method, which builds on earlier work of Cervera et al (1997), scans possible speeds in the Fresnel distance - time domain with a dynamic, sliding window and derives a best-speed estimate from the resultant speed distribution. We test the performance of our method, called pre-$t_{0}$ speeds by sliding-slopes technique (PSSST), on transverse scattered meteor echoes observed by the Middle Atmosphere Alomar Radar System (MAARSY) and the Canadian Meteor Orbit Radar (CMOR), and compare the results to time-of-flight and Fresnel transform speed estimates. Our novel technique is shown to produce good results when compared to both model and speed measurements using other techniques. We show that our speed precision is $\pm$5$\%$ at speeds less than 40 km/s and we find that more than 90$\%$ of all CMOR multi-station echoes have PSSST solutions. For CMOR data, PSSST is robust against the selection of critical phase value and poor phase unwrapping. Pick errors of up to $\pm$6 pulses for meteor speeds less than about 50 km/s produce errors of less than $\pm$5$\%$ of the meteoroid speed. In addition, the width of the PSSST speed Kernel density estimate (KDE) is used as a natural measure of uncertainty that captures both noise and $t_0$ pick uncertainties.