Statistical discrimination of global post-seismic ionosphere effects under geomagnetic quiet and storm conditions

Gulyaeva, T.L.; Arıkan, Feza

doi:10.1080/19475705.2016.1246483

Cited by 19 publications

(5 citation statements)

References 58 publications

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“…In terms of the geomagnetic condition, the mean Kp and mean Dst were 2.5 and −13.0 nT, respectively. These two values are below the thresholds for active geomagnetic conditions, which are larger than 3 for Kp index (Mungufeni et al, 2016) and smaller than −20 nT for Dst index (Gulyaeva & Arikan, 2017). In terms of solar condition, the F10.7 index varied smoothly with a mean of 84.3 sfu, which is also below the threshold of active solar conditions, that is, 150 sfu (Tapping, 2013).…”

Section: Geomagnetic and Solar Conditionsmentioning

confidence: 98%

Ionospheric Disturbances Observed From a Single GPS Station in Hong Kong During the Passage of Super Typhoon Hato in 2017

Liu

Lee

2022

Space Weather

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The powerful convection in the lower atmosphere, for example, tropical cyclones (TCs), has a high probability of causing ionospheric disturbances. We observed evident ionospheric disturbances during the passage of Super Typhoon Hato in 2017 by analyzing the highest elevation vertical total electron content (HeVTEC) time series, that is, the VTEC from the Global Positioning System (GPS) satellite with the highest elevation at each epoch, retrieved from a single GPS station in Hong Kong. The results demonstrate that the daily maximum of HeVTEC time series on each day during the TC period experienced a large fluctuation ranging from 33.5 TEC unit (TECU) to the peak value 62.0 TECU. The peak value 62.0 TECU occurred on the TC landfall day 23 August 2017, approximately twice as high as that of non‐TC‐impacted days. We also found that the TEC spatial gradients above the landfall area increased by around 50% and 200% in the north‐to‐south and west‐to‐east directions, respectively. We also examined the daily mean bias (MB) of VTEC above Hong Kong with respect to the mean VTEC during the past 27 days from 20 July to 15 August in 2017. The largest VTEC MB was observed in the west of the landfall area along the TC moving direction, on the TC landfall day. Our findings provided the evidence that the Hato's landfall over the coast near Hong Kong caused the apparent ionospheric disturbances above the landfall area.

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Section: Geomagnetic and Solar Conditionsmentioning

confidence: 98%

Ionospheric Disturbances Observed From a Single GPS Station in Hong Kong During the Passage of Super Typhoon Hato in 2017

Liu

Lee

2022

Space Weather

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“…Firstly, the variations of Kp, Dst, and F10.7 (Rostoker 1972) indices during the Usagi were studied because the strong geomagnetic and solar activities can also induce the ionospheric disturbance (Moeketsi et al 2007;Brunini and Azpilicueta 2010;Guo et al 2017). For a quiet geomagnetic condition, the Kp index should be smaller than 3 (Mungufeni et al 2016), and the Dst index should be larger than − 20 nT (Gulyaeva and Arikan 2017). For a relatively quiet solar condition, the F10.7 index should be smaller than 150 sfu (Tapping 2013).…”

Section: Ionospheric Conditionmentioning

confidence: 99%

The ionospheric condition and GPS positioning performance during the 2013 tropical cyclone Usagi event in the Hong Kong region

Liu

2021

Earth Planets Space

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The ionosphere plays a critical role in the electromagnetic waves in communication systems such as the global positioning system (GPS). However, it is suspected that the strong convection during the tropical cyclone (TC) events can be a trigger to anomalous electron density variation in the ionosphere. This study analyzed the variation of three ionosphere-related parameters based on the GPS data including scintillation index S4, cycle slips, and total electron content (TEC) rate (TECR) during the tropical cyclone event (the 2013 TC Usagi) in the Hong Kong region. The results showed that the ionosphere-related parameters had a consistent significant increase on the second day after the Usagi made landfall near Hong Kong. Consequently, the positioning performance of GPS precise point positioning (PPP) and relative positioning modes was degraded. The degradation was ~ 138%, ~ 181%, and ~ 460% in the east (root mean square (RMS) 0.050 m), north (RMS 0.045 m), and up (RMS 0.185 m), respectively, compared with the RMS of 0.021 m in the east, 0.016 m in the north, and 0.033 m in the up on the normal day. Regarding the relative positioning, the positioning errors in the east (RMS 0.134 m) and north (RMS 0.118 m) directions were ~ 7.1 and ~ 7.9 times, respectively, as large as the RMS of 0.019 m in the east and 0.015 m in the north on the normal day. The positioning errors in the up (RMS 0.513 m) direction were ~ 12.2 times larger than the RMS of 0.042 m on the normal day.

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“…State of the ionosphere changes due to geomagnetic activity (Mandea & Korte, 2010). It is important to distinguish between extreme behavior in ionospheric state due to geomagnetic storms and seismic activity (Gulyaeva & Arikan, 2017; Gulyaeva et al, 2016, 2017a). Ionosphere‐based parameters are widely used to assess the state of the ionosphere in terms of geomagnetic activities and geomagnetic storms.…”

Section: Data Used By the Proposed Eq‐pd Techniquementioning

confidence: 99%

A Machine Learning‐Based Detection of Earthquake Precursors Using Ionospheric Data

Akyol

Arıkan

2020

Radio Science

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Detection of precursors of strong earthquakes is a challenging research area. Recently, it has been shown that strong earthquakes affect electron distribution in the regional ionosphere with indirectly observable changes in the ionospheric delays of GPS signals. Especially, the total electron content (TEC) estimated from GPS data can be used in the seismic precursor detection for strong earthquakes. Although physical mechanisms are not well understood yet, GPS‐based seismic precursors can be observed days prior to the occurrence of the earthquake. In this study, a novel machine learning‐based technique, EQ‐PD, is proposed for detection of earthquake precursors in near real time based on GPS‐TEC data along with daily geomagnetic indices. The proposed EQ‐PD technique utilizes support vector machine (SVM) classifier to decide whether an observed spatiotemporal anomaly is related to an earthquake precursor or not. The data fed to the classifier are composed of spatiotemporal variability map of a region. Performance of the EQ‐PD technique is demonstrated in a case study over a region covering Italy in between the dates of 1 January 2014 and 30 September 2016. The data are partitioned into three nonoverlapping time periods, that are used for training, validation, and test of detecting precursors of earthquakes with magnitudes above 4 in Richter scale. The EQ‐PD technique is able to detect precursors in 17 out of 21 earthquakes while generating 7 false alarms during the validation period of 266 days and 22 out of 24 earthquakes while generating 13 false alarms during the test period of 282 days.

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Statistical discrimination of global post-seismic ionosphere effects under geomagnetic quiet and storm conditions

Cited by 19 publications

References 58 publications

Ionospheric Disturbances Observed From a Single GPS Station in Hong Kong During the Passage of Super Typhoon Hato in 2017

Ionospheric Disturbances Observed From a Single GPS Station in Hong Kong During the Passage of Super Typhoon Hato in 2017

The ionospheric condition and GPS positioning performance during the 2013 tropical cyclone Usagi event in the Hong Kong region

A Machine Learning‐Based Detection of Earthquake Precursors Using Ionospheric Data

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