The first comparative analysis of meteor echo and sporadic scattering identified by a self-learned neural network in EKB and MAGW ISTP SB RAS radar data

Abstract

The paper describes the current version (v.1.1) of the algorithm for automatic classification of signals received by ISTP SB RAS decameter coherent scatter radars. The algorithm is a self-learning neural network that determines the type of scattered signals from the results of physical modeling of radio wave propagation, using radar data and international reference models of the ionosphere and geomagnetic field. According to MAGW and EKB ISTP SB RAS radar data for 2021, the algorithm self-learns to classify scattered signals into initially unknown classes based on physically interpreted parameters of radio wave propagation and data measured by the radar, with 15 frequently observed out of 20 possible hidden classes identified, 14 of which can be interpreted from a physical point of view. To demonstrate the operation of the algorithm, we present the first statistical analysis of observations of signals assigned by the algorithm to classes which we interpret as scattering by meteor trails and scattering with the sporadic E layer respectively. Through a statistical analysis of EKB and MAGW radar data during 2021–2022, we demonstrate the range-altitude characteristics of signals of these types. A correlation is shown between the hourly average numbers of observations of both classes, as well as between the hourly average line-of-sight velocities obtained for both classes. The results obtained make it possible to interpret these classes as a meteor echo and sporadic scattering respectively, and to use radar data to study the interaction between the neutral atmosphere (studied from meteor scattering data) and the lower ionosphere (studied from observations of sporadic scattering). Currently, this classification algorithm works in ISTP SB RAS radars in automatic mode.