Automated Detection and Characterization of Wave Structures Obtained From GNSS Measurements

Abstract

Abstract A simple technique designed to automatically identify and characterize wave structures from total electron content (TEC) data obtained from Global Navigation Satellite System (GNSS) satellites and multiple receiver stations is presented. We used 11 years of GNSS data (one complete solar cycle) to detect and characterize the traveling ionospheric disturbances (TIDs) (or wave structures) over high latitude (65°N ± 5°, 147°W ± 5°—Poker Flat, AK) and middle latitude (40°N ± 5°, 117°W ± 5°—Mt. Moses, NV) regions. The algorithm is capable of automatically detecting basic wave parameters such as wave period, horizontal phase velocity, amplitude, wave propagation direction, and wavelength. The designed algorithm can be applicable in the following areas: (a) global GNSS‐TEC (b) climatology of waves (c) input into innovative machine learning algorithms, such as ABCGAN (e.g., Valentic, 2023, https://doi.org/10.5281/zenodo.7747377 designed to characterize background ionospheric plasma and predict wave‐like high/low‐frequency perturbations) (d) anomaly detection for real‐time scenarios. Furthermore, we apply the developed wave detect and characterization technique to investigate three rockets launched on 26 and 28 January 2015. We examine the distribution of different scales of TIDs, and how they varied from high (Poker Flat) to middle (Mt. Moses) latitudes. Lastly, we show that the wave structures at the high‐latitude regions of Poker Flat are substantially affected by auroral processes and those from the middle‐latitude regions of Mt. Moses are impacted by AGWs coupling from below.

Keywords

• automated wave detection
• GNSS‐TEC
• LSTIDs
• MSTIDs
• wave parameters
• differential TEC estimation method