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This paper examines the performances of NeQuick2, the latest available IRI-2016, IRI-2012 and IRI-2007 models in describing the monthly and seasonal mean total electron content (TEC) over the East African region. This is to gain insight into the success of the various model types and versions at characterizing the ionosphere within the equatorial ionization anomaly. TEC derived from five Global Positioning System (GPS) receivers installed at Addis Ababa (ADD, 5.33°N, 111.99°E Geog.), Asab (ASAB, 8.67°N, 116.44°E Geog.), Ambo (ABOO, 5.43°N, 111.05°E Geog.), Nairobi (RCMN, ?4.48°N, 108.46°E Geog.) and Nazret (NAZR, 4.78°N, 112.43°E Geog.), are compared with the corresponding values computed using those models during varying solar activity period (1998 and 2008–2015). We found that different models describe the equatorial and anomaly region ionosphere best depending on solar cycle, season and geomagnetic activity levels. Our results show that IRI-2016 is the best model (compared to others in terms of discrepancy range) in estimating the monthly mean GPS-TEC at NAZR, ADD and RCMN stations except at ADD during 2008 and 2012. It is also found that IRI-2012 is the best model in estimating the monthly mean TEC at ABOO station in 2014. IRI show better agreement with observations during June solstice for all the years studied at ADD except in 2012 where NeQuick2 better performs. At NAZR, NeQuick2 better performs in estimating seasonal mean GPS-TEC during 2011, while IRI models are best during 2008–2009. Both NeQuick2 and IRI models underestimate measured TEC for all the seasons at ADD in 2010 but overestimate at NAZR in 2009 and RCMN in 2008. The periodic variations of experimental and modeled TEC have been compared with solar and geomagnetic indices at ABOO and ASAB in 2014 and results indicate that the F10.7 and sunspot number as indices of solar activity seriously affects the TEC variations with periods of 16–32?days followed by the geomagnetic activity on shorter timescales (roughly periods of less than 16?days). In this case, NeQuick2 derived TEC shows better agreement with a long term period variations of GPS-TEC, while IRI-2016 and IRI-2007 show better agreement with observations during short term periodic variations. This indicates that the dependence of NeQuick2 derived TEC on F10.7 is seasonal. Hence, we suggest that representation of geomagnetic activity indices is required for better performance over the low latitude region.  相似文献   
2.
This paper investigates the capacity of the latest version of the International Reference Ionosphere (IRI-2012) model in predicting the vertical Total Electron Content (vTEC) over Ethiopian regions during solar minimum (2009) and solar maximum (2013) phases. This has been carried out by comparing the IRI-2012 modeled and experimental vTEC inferred from eight ground based dual frequency GPS (Global Positioning System) receivers installed recently at different regions of the country. In this work, the diurnal, monthly and seasonal variation in the measured vTEC have been analyzed and compared with the IRI-2012 modeled vTEC. During the solar minimum phase, the lowest and highest diurnal peak of the experimental vTEC are observed in July and October, respectively. In general, the diurnal variability of vTEC has shown minimum values around 0300 UT (0600 LT) and maximum values between around 1000 and 1300 UT (1300 and 1600 LT) during both solar activity phases. Moreover, the maximum and minimum monthly and seasonal mean hourly vTEC values are observed in October and July and in the March equinox and June solstice, respectively. It is also shown that the IRI-2012-model better predicts the diurnal vTEC in the time interval of about 0000–0300 UT (0300–0600 LT) during the solar minimum phase. However, the model generally overestimates the diurnal vTEC except in the time interval of about 0900–1500 UT (1200–1800 LT) during the solar maximum phase. The overall result of this work shows that the diurnal vTEC prediction performance of the model is generally better during the solar minimum phase than during solar maximum phase. Regarding the monthly and seasonal prediction capacity of the model, there is a good agreement between the modeled and measured monthly and seasonal mean hourly vTEC values in January and December solstice, respectively. Another result of the work depicts that unlike the GPS–TEC the IRI-2012 TEC does not respond to the effect resulted from geomagnetic storms.  相似文献   
3.
Post-sunset ionospheric irregularities are common features of the equatorial ionosphere that affect radio communication and navigation systems; their triggering physical mechanism is not yet fully understood. Atmospheric gravity wave is considered as a seeding mechanism for the occurrence of ionospheric irregularities (Abdu et al., 2009). To understand the effects of atmospheric waves, characteristics of wavelike oscillation from ionospheric total electron content (TEC) fluctuation that can be obtained from superposition of different oscillation modes have been investigated. Decomposing fluctuating TEC into different oscillation modes and investigating oscillation characteristics of each component is also important to get insight about the characteristics of individual atmospheric waves that may cause TEC fluctuation. In this paper we have investigated characteristics of components of fluctuating TEC obtained from SCINDA GPS receiver installed at Bahir Dar, (geographic coordinate, 11.5°N, 37.6° E, and dip latitude of 2.5°N) Ethiopia during April 2012. First Empirical Mode Decomposition (EMD) has been applied to decompose TEC fluctuation into different oscillation modes that are known as Intrinsic Mode Function (IMF). Hilbert-Huang Transform (HHT) and Continuous Wavelet Transform (CWT) have been applied to investigate the characteristics of wave-like oscillations. Applying EMD on fluctuating vTEC corresponding to a GPS satellite, five components are found. Results from HHT and CWT have shown excellent agreement. In addition, it is found out that the median periods of oscillation of those five components are 9, 17, 47, 78, and 118 min. Of these periods, 17 and 47 min respectively are oscillation periods of components of TEC fluctuation with occurrence frequency of 92% and 91% that may be interpreted as the manifestation of two frequently occurring components of atmospheric gravity waves that are likely generated by the motion of solar terminator.  相似文献   
4.
We model regular and irregular variation of ionospheric total electron content as stationary and non-stationary processes, respectively. We apply the method developed to SCINDA GPS data set observed at Bahir Dar, Ethiopia 11.6°N,37.4°E. We use hierarchical Bayesian inversion with Gaussian Markov random process priors, and we model the prior parameters in the hyperprior. We use Matérn priors via stochastic partial differential equations, and use scaled Inv-χ2 hyperpriors for the hyperparameters. For drawing posterior estimates, we use Markov Chain Monte Carlo methods: Gibbs sampling and Metropolis-within-Gibbs for parameter and hyperparameter estimations, respectively. This allows us to quantify model parameter estimation uncertainties as well. We demonstrate the applicability of the method proposed using a synthetic test case. Finally, we apply the method to real GPS data set, which we decompose to regular and irregular variation components. The result shows that the approach can be used as an accurate ionospheric disturbance characterization technique that quantifies the total electron content variability with corresponding error uncertainties.  相似文献   
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