Enabling statistical analysis of the main ionospheric trough with computer vision

• Main Author: Starr, Gregory Walter Sidor
• Other Authors: Semeter, Joshua L.
• Language: en_US
• Published: 2021
• Subjects: Electrical engineering; Data science; Ionosphere; Machine learning; Magnetosphere
• Online Access: https://hdl.handle.net/2144/43082

The main ionospheric trough (MIT) is a key density feature in the mid-latitude ionosphere and characterizing its structure is important for understanding GPS radio signal scintillation and HF wave propagation. While a number of previous studies have statistically investigated the properties of the trough, they have only examined its latitudinal cross sections, and have not considered the instantaneous two-dimensional structure of the trough. In this work, we developed an automatic optimization-based method for identifying the trough in Total Electron Content (TEC) maps and quantified its agreement with the algorithm developed in (Aa et al., 2020). Using the newly developed method, we created a labeled dataset and statistically examined the two-dimensional structure of the trough. Specifically, we investigated how Kp affects the trough’s occurrence probability at different local times. At low Kp, the trough tends to form in the postmidnight sector, and with increasing Kp, the trough occurrence probability increases and shifts premidnight. We explore the possibility that this is due to increased occurrence of troughs formed by subauroral polarization streams (SAPS). Additionally, using SuperDARN convection maps and solar wind data, we characterized the MIT's dependence on the interplanetary magnetic field (IMF) clock angle.