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This paper discusses the diurnal and seasonal variations of the F2 layer critical frequency (foF2) and the improvement of performance of the IRI-2016 model in predicting foF2 over three latitudes in different hemispheres during low and high solar activities. We extracted the foF2 data from six ionosonde stations which are Manila (14.7 degrees N, 121.1 degrees E), Yamagawa (31.2 degrees N, 130.6 degrees E), Yakutsk (62.0 degrees N,129.6 degrees E), Townsville (19.6 degrees S, 146.8 degrees E), Hobart (42.9 degrees S, 147.3 degrees E) and Terre Adelie (66.6 degrees S, 140.0 degrees E). The data of both low solar activity (LSA) period and high solar activity (HSA) periods were divided into three seasons as Northern Summer (May, June, July and August), Equinoxes (March, April, September and October) and Northern Winter (November, December, January and February). The present study showed that the IRI-2016 performance is strongly dependent on the solar activity, latitude, season, local time and hemisphere. For both hemispheres, the foF2 values at low latitude station are larger than those at middle latitude station, whereas the foF2 values at middle latitude station are larger than those at high latitude station. The agreement between IRI2016-modelled foF2 and foF2 measurements on all stations selected in the northern hemisphere is best for North Summer and worst for North Winter. For northern hemisphere, the values of relative deviations during both solar activities are largest in high latitudes and smallest in middle latitudes. As for southern hemisphere, the values of relative deviations during LSA are largest in middle latitudes and smallest in high latitudes, whereas the values of relative deviations during HSA are largest in low latitudes and smallest in high latitudes. It is thought that the relative deviations in the observed foF2 values are caused by solar activity that strongly alter chemical and electromagnetic processes in the ionosphere. These results are important for future improvements depending on solar activity and seasons in the IRI model for foF2 values over three latitudes in different hemispheres.

The ionosphere and its activities play an important role in radio communications, space science and positioning. In this paper, we present efficiency of the adaptive neuro-fuzzy inference system (ANFIS) in ionosphere time series prediction at severe solar activity periods. The high accuracy prediction of the ionosphere total electron content (TEC) using ANFIS is the main purpose of the paper. Also, investigation of the daily, monthly and annually temporal behavior of the ionosphere is another goal of the paper. Three GPS stations (ANKR, ANTL and ZONG) from Turkish permanent GPS network (TPGN) are selected for analysis. These stations are at mid-latitudes. To predict and analyze ionospheric temporal behavior, observations of the 2014–2015 are selected. The index of solar activity in these 2 years is the maximum. Training of ANFIS is done by back-propagation (BP) algorithm. In order to analyze the accuracy of the proposed method, testing step has been performed in different days, months and seasons and compared with the global ionosphere map (GIM) TEC as a traditional ionosphere model, as well as, international reference ionosphere 2016 (IRI2016) and artificial neural network (ANN) models. To validate the results, root mean square error (RMSE), correlation coefficient, relative error, dTEC = |TECGPS-TECMODEL| and residual histogram are used. At testing step, the maximum correlation coefficient obtained 0.869 for the ANFIS. Also, the minimum RMSE of 3.131 TECU is computed for ANFIS at testing step. Using the ANFIS model, the daily, monthly and annual variations of the ionosphere time series are revealed with high accuracy. The results show that the ANFIS model is a reliable and high-precision alternative to the conventional ionosphere models in Turkish region.

Earthquake (EQ) anomalies in the form of enhancement and depletion in ionospheric Total Electron Content (TEC) from Global Positioning System (GPS) may considerably alarm about short and long term precursors of the impending main shock. In this paper, TEC anomalies are investigated from permanent GPS ground-stations in Turkey associated to Mw ≥ 6.0 EQs occurred in 2011–2012. Temporal and spatial analyses of TEC at 2 h sampling have shown significant evidences about EQ induced ionospheric anomalies during 10–14 h of UT (Universal Time) within 5 days before Mw 6.0 Greece, and Mw 7.1, Turkish EQ. Spatial analyses have manifested arrival of TEC anomalies at UT = 10 h to epicenter of both EQs, which linger above epicenter during UT = 12–14 h and left seismogenic zone after UT = 14 h before every EQ during Kp

This paper describes the investigation and comparison of two consecutive geomagnetic storms of Solar Cycle 24. The first storm is moderate (Dst = −50 nT) and occurred on 22 April 2017, while the second storm is intense (Dst = −125 nT) and occurred on 28 May 2017. These storms are compared on the basis of solar wind parameters (Bz, E, P, N, v, and T) and zonal geomagnetic indices (Dst, ap, and AE). Mathematical analysis is performed with a data set of 9 variables obtained from NASA's observations. Descriptive statistics, which demonstrates the power of binary relationships with a covariance matrix; hierarchical clustering with a dendrogram; and factor analysis pointing to three separate eigenvectors are the primary tools of our study analysis. From the results, linear models are established from the cause-effect relationship of zonal geomagnetic indices with solar wind parameters governed by the causality principle. Finally, nonlinear models are also established with solar wind pressure (P), proton density (N), and ap index. In all analyses, the causality principle is obeyed rigidly and the mathematical models are discussed in terms of the cause-effect relationship.

On August 11, 2012, two successive strong earthquakes (Mw = 6.4 and 6.3) hit the East Azerbaijan Province, NW Iran at 12:23 and 12:34 UTC. This study investigated the variations of multi earthquakes precursors, (Land Surface Temperature (LST), Total Electron Content (TEC), Outgoing Longwave Radiation (OLR), and Air Temperature (AT) of non-seismic phenomena before and after the successive Ahar (Iran) earthquakes. In this study, we used the moving median method to detect the anomalies in TEC, whereas ±2σ (95% confidence level) interval was used in MODIS LST and OLR analyses as an anomaly indicator. The results indicated that the MODIS Nighttime LST decreased by 5.5–6.3 °C compared to the mean (LST) within 10–12 days before the earthquakes Then a dramatic increase was observed on August 02, 2012. The LST results were consistent with the results of OLR, where the anomaly was observed on July 29, 2012. In addition to LST and OLR, significant TEC anomalous disturbances (increase and decrease) occurred two weeks before the earthquakes started on 28th July and reached the highest value 3 days before the earthquakes. Furthermore, AT analysis also provided identical results with LST behaviors. These results were instructive for seismologists and represent the importance of the corresponding potential precursors for upcoming earthquakes.

This study aims to investigate the behaviors of the potential earthquake precursors such as Total Electron Content (TEC), daytime/nighttime Land Surface Temperature (LST) and aerosols before and after the 2013 Mw 7.7 Awaran (Pakistan) earthquake. TEC values were obtained from Global Positioning System (GPS) measurements, and LST and aerosol values were retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard the Terra and Aqua satellites, respectively. In order to detect the anomalies, moving median method was used in TEC analyses and ±2σ (95% confidence level) interval was utilized in LST and Aerosol Optical Depth (AOD) analyses as anomaly indicator. TEC anomalies were identified 3 days (21.09.2013) and 5 days (19.09.2013) before the earthquake. In daytime/nighttime LST analyses, three regions of interest for spatial analyses were determined including epicenter, 10 km impact area around the epicenter and Modified Mercalli Intensity (MMI) 7.5 contour which was presented in the United States Geological Survey (USGS)'s shake map. Different anomalous days of LST were observed from these three regions of interest. In AOD analysis, only MMI 7.5 contour was considered due to its low spatial resolution (10 × 10km pixel size). The anomaly in AOD values was observed on 29.09.2013, 5 days after the earthquake. However, the abnormal behavior of AOD on 21.09.2013 was consistent with GPS-TEC anomaly. Furthermore, considering daytime/nighttime LST values on epicenter and 10 km impact area, we observed a systematic increase culminated near the earthquake event and a decrease after the earthquake. On the contrary, a systematic decrease and then increase of the AOD values were identified. The obtained results showed that TEC, LST and AOD were important potential precursors for Earthquake prediction; however, further developments on methods, data types and statistical analyses are still required.