Browsing by Author "Fajardo, G."
Now showing 1 - 3 of 3
Results Per Page
Sort Options
Item Open Access Exploration of machine learning tools developed for the study of space weather and its impact on position approximation in GNSS systems(Instituto Geofísico del Perú, 2021-06) Fajardo, G.; Pacheco, Edgardo E.The equatorial ionosphere has been extensively studied using purely physical models, however in recent years, with a large amount of data, it has been possible to improve these models using machine learning techniques. In this paper, we share the research results aimed to evaluate the influence of space weather parameters on GPS position approximation. We evaluated data from the Huancayo GPS station between 2016 and 2020 and we have taken into account the space weather data from the OMNI website, scintillation index (S4) and position data obtained from the GPS of the LISN network to perform our model. In addition, we use tropospheric conditions provided by the Geophysical Institute of Peru (IGP). The final result is a reliability matrix obtained with an XG Boost algorithm that will allow us to evaluate if a GPS signal given the conditions is indeed reliable or not.Item Open Access Exploring the correlation between ionospheric scintillation and GNSS positioning error near the magnetic equator using machine learning techniques(Instituto Geofísico del Perú, 2020-06) Fajardo, G.; Pacheco, Edgardo E.Ionospheric scintillations are a common phenomenon in the equatorial ionosphere. This phenomenon directly affects the position estimated by GNSS receivers degrading the quality of the radio signals; however, the quantification of the positioning error contributed by the ionosphere over the Peruvian sector has not been studied in detail. In this work, algorithms are being implemented that will allow us to identify and classify amplitude scintillation (S4) levels, we have worked with data from the Huancayo Observatory for the period December 2016-February 2017 obtained from LISN, this data has been plotted to analyze the spatial and temporal occurrence, and to analyze the occurrence of S4 as a function of other space weather variables obtained from OMNI2. The machine learning algorithms were decision tree, Support Vector Machine (SVM), Neuronal Network (NN). Decision tree was implemented as a filtering method, support vector machine for clustering and neuronal network to generate time series in forecasting. This paper shows the initial part of an investigation that aims to correlate qualitatively and quantitatively the occurrence of amplitude scintillations (S4) with errors in the position estimation of GNSS receivers, once the correlation between S4 and position error has been quantified, it may be possible to predict the error by predicting S4.Item Open Access The impact of the Hunga Tonga–Hunga Ha’apai volcanic eruption on the Peruvian atmosphere: from the sea surface to the ionosphere(SpringerOpen, 2024-05-28) Pacheco, Edgardo E.; Velasquez, J. P.; Flores, R.; Condori, L.; Fajardo, G.; Kuyeng, Karim; Scipión, Danny; Milla, M.; Conte, J. F.; Poblet, F. L.; Chau, J. L.; Suclupe, J.; Rojas, R.; Manay, E.The eruption of the Hunga Tonga Hunga Ha’apai volcano on 15 January 2022 significantly impacted the lower and upper atmosphere globally. Using multi-instrument observations, we described disturbances from the sea surface to the ionosphere associated with atmospheric waves generated by the volcanic eruption. Perturbations were detected in atmospheric pressure, horizontal magnetic field, equatorial electrojet (EEJ), ionospheric plasma drifts, total electron content (TEC), mesospheric and lower thermospheric (MLT) neutral winds, and ionospheric virtual height measured at low magnetic latitudes in the western South American sector (mainly in Peru). The eastward Lamb wave propagation was observed at the Jicamarca Radio Observatory on the day of the eruption at 13:50 UT and on its way back from the antipodal point (westward) on the next day at 07:05 UT. Perturbations in the horizontal component of the magnetic field (indicative of EEJ variations) were detected between 12:00 and 22:00 UT. During the same period, GNSS-TEC measurements of traveling ionospheric disturbances (TIDs) coincided approximately with the arrival time of Lamb and tsunami waves. On the other hand, a large westward variation of MLT winds occurred near 18:00 UT over Peru. However, MLT perturbations due to possible westward waves from the antipode have not been identified. In addition, daytime vertical plasma drifts showed an unusual downward behavior between 12:00 and 16:00 UT, followed by an upward enhancement between 16:00 and 19:00 UT. Untypical daytime eastward zonal plasma drifts were observed when westward drifts were expected. Variations in the EEJ are highly correlated with perturbations in the vertical plasma drift exhibiting a counter-equatorial electrojet (CEEJ) between 12:00 and 16:00 UT. These observations of plasma drifts and EEJ are, so far, the only ground-based radar measurements of these parameters in the western South American region after the eruption. We attributed the ion drift and EEJ perturbations to large-scale thermospheric wind variations produced by the eruption, which altered the dynamo electric field in the Hall and Pedersen regions. These types of multiple and simultaneous observations can contribute to advancing our understanding of the ionospheric processes associated with natural hazard events and the interaction with lower atmospheric layers.