On estimating the phase scintillation index using TEC provided by ISM and IGS professional GNSS receivers and machine learning
Author(s)
Language
English
Obiettivo Specifico
OSA3: Climatologia e meteorologia spaziale
Status
Published
JCR Journal
JCR Journal
Journal
Issue/vol(year)
7/73 (2024)
ISSN
0273-1177
Publisher
Elsevier
Pages (printed)
3753-3771
Date Issued
2024
Alternative Location
Abstract
Amplitude and phase scintillation indexes (S4 and SigmaPhi) provided by Ionospheric Scintillation Monitoring (ISM) receivers are the most used GNSS-based indicators of the signal fluctuations induced by the presence of ionospheric irregularities. These indexes are available only from ISM receivers which are not as abundant as other types of professional GNSS receivers, resulting in limited geographic distribution. This makes the scintillation indexes measurements rare and sparse compared to other types of ionospheric measurements available from GNSS receivers. Total Electron Content (TEC), on the other hand, is an ionospheric parameter available from a wide range of multi-frequency GNSS receivers. Many efforts have worked on establishing scintillation indicators based on TEC, and geodetic receivers in general, introducing various metrics, including the Rate of TEC change (ROT) and ROT Index (ROTI). However, a possible relationship between TEC and its variation, and the corresponding scintillation index that an Ionospheric Scintillation Monitor (ISM) receiver would estimate is not trivial. In principle, TEC can be retrieved from carrier phase measurements of the GNSS receiver, as
. We investigate how to estimate SigmaPhi from time series of TEC and ROT measurements from an ISM in Ny-Ålesund (Svalbard) using Machine Learning (ML). To evaluate its usability to estimate SigmaPhi
from geodetic receivers, the model is tested using TEC data provided by a quasi-co-located geodetic receiver belonging to the International GNSS Service (IGS) network. It is shown that the model performance when TEC from the IGS receiver is used gives comparable results to the model performance when TEC from the ISM receiver is utilised. The model's ability to infer the exact value of the scintillation index is bound to Mean Square Error (MSE) = 0.1 radians^2 when SigmaPhi < 0. 8 radians. For SigmaPhi > 0. 8 radians the MSE reaches 0.18 and 0.45 radians^2 in operative testing using ISM and IGS measurements, respectively. However, the model’s ability to detect phase scintillation from IGS TEC measurements is comparable to expert visual inspection. Such a model has potential in alerting against phase fluctuations resulting in enhanced SigmaPhi, especially in locations where ISM receivers are not available, but other types of dual-frequency GNSS receivers are present.
. We investigate how to estimate SigmaPhi from time series of TEC and ROT measurements from an ISM in Ny-Ålesund (Svalbard) using Machine Learning (ML). To evaluate its usability to estimate SigmaPhi
from geodetic receivers, the model is tested using TEC data provided by a quasi-co-located geodetic receiver belonging to the International GNSS Service (IGS) network. It is shown that the model performance when TEC from the IGS receiver is used gives comparable results to the model performance when TEC from the ISM receiver is utilised. The model's ability to infer the exact value of the scintillation index is bound to Mean Square Error (MSE) = 0.1 radians^2 when SigmaPhi < 0. 8 radians. For SigmaPhi > 0. 8 radians the MSE reaches 0.18 and 0.45 radians^2 in operative testing using ISM and IGS measurements, respectively. However, the model’s ability to detect phase scintillation from IGS TEC measurements is comparable to expert visual inspection. Such a model has potential in alerting against phase fluctuations resulting in enhanced SigmaPhi, especially in locations where ISM receivers are not available, but other types of dual-frequency GNSS receivers are present.
Type
article
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