Supervised Detection of Ionospheric Scintillation in Low-Latitude Radio Occultation Measurements

Abstract

Global Navigation Satellite System (GNSS) Radio Occultation (RO) has provided high-quality atmospheric data assimilated in Numerical Weather Prediction (NWP) models and climatology studies for more than 20 years. In the satellite–satellite GNSS-RO geometry, the measurements are susceptible to ionospheric scintillation depending on the solar and geomagnetic activity, seasons, geographical location and local time. This study investigates the application of the Support Vector Machine (SVM) algorithm in developing an automatic detection model of F-layer scintillation in GNSS-RO measurements using power spectral density (PSD). The model is intended for future analyses on the influence of space weather and solar activity on RO data products over long time periods. A novel data set of occultations is used to train the SVM algorithm. The data set is composed of events at low latitudes on 15–20 March 2015 (St. Patrick’s Day geomagnetic storm, high solar flux) and 14–19 May 2018 (quiet period, low solar flux). A few conditional criteria were first applied to a total of 5340 occultations to define a set of 858 scintillation candidates. Models were trained with scintillation indices and PSDs as training features and were either linear or Gaussian kernel. The investigations also show that besides the intensity PSD, the (excess) phase PSD has a positive contribution in increasing the detection of true positives.

Automatic GNSS Ionospheric Scintillation Detection with Radio Occultation Data Using Machine Learning Algorithm

“…In particular, this study uses occultation events for which scintillation has been orded [37], and these occultation events are labelled by setting label 1 for scintilla events and label 0 for non-scintillation events. The average duration of an occulta event is 22 s, which means that the power spectral density of the satellite signal is c puted within an average time window of 22 s and is input into the classification mod a data sample along with the labelled values of the corresponding occultation events ble 2 provides an overview of the detailed structure of this dataset, with the first col being the class labels and the second to last column being the PSD computed using Welch method, with each row constituting a row vector entered into the model as a sample.…”

“…In particular, this study uses occultation events for which scintillation has been recorded [37], and these occultation events are labelled by setting label 1 for scintillation events and label 0 for non-scintillation events. The average duration of an occultation event is 22 s, which means that the power spectral density of the satellite signal is computed within an average time window of 22 s and is input into the classification model as a data sample along with the labelled values of the corresponding occultation events.…”

Design of Novel Reconfigurable Single-Board Satellite for Enhanced Space Environment Detection

“…As illustrated in Figure 2, cost-effective ionospheric occultation detection requires the use of GNSS units to record pseudo-range and phase data, which are subsequently transmitted to the ground for ionospheric electron density inversion [23,24].…”

“…As illustrated in Figure 2, cost-effective ionospheric occultation detection requires the use of GNSS units to record pseudo-range and phase data, which are subsequently transmitted to the ground for ionospheric electron density inversion [23,24]. Upon analysis, it becomes evident that the internal very small satellites within a balloon satellite must, at a minimum, incorporate the following components:…”

Study of Ionospheric Bending Angle and Scintillation Profiles Derived by GNSS Radio-Occultation with MetOp-A Satellite

“…A validation in terms of electron density data observed by MetOp-A is provided by [36]. In the present work, we carry out the validation directly over the bending angle [37,38] and signal fading observations [39][40][41], providing additional validation over the MetOp-A dataset. Details of the dataset collected by the MetOp-A satellite and other RO missions used for comparisons are presented in Section 2.…”