Relevant bibliographies by topics / Ionospheric variability

Academic literature on the topic 'Ionospheric variability'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Ionospheric variability"

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Pandit, D., N. P. Chapagain, and B. Adhikari. "Study of Ionospheric Variability During Super Substorms." Journal of Nepal Physical Society 6, no. 2 (December 31, 2020): 74–84. http://dx.doi.org/10.3126/jnphyssoc.v6i2.34862.

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This paper study variability of three ionospheric parameters foF2, h′F and hmF2 to investigate the middle latitude ionospheric effect at Boulder, Colorado, USA (40°N, l105.0° W) during super substorms (SSSs) of 24 August 2005, and 7 September 2017 and 8 September 2017 respectively. Continuous wavelet transform (cwt) implemented to identify the low and high frequency and longer and shorter duration present in the signal. The result shows decrease in foF2 during SSSs of 24 August 2005 and 8 September 2017 and increase in foF2 during 7 September 2017. The highest fluctuation in h′F is noticed during SSS of 24 August 2005. The cwt shows that the coupling between solar wind and magnetosphere occurs between ~ 16 to 32 minutes for SSS of 24 August 2005 and between 27.9 to 64 minutes during super substorm of 7 and 8 September 2017 for all the ionospheric parameters respectively. This study leads to understand the impact of SSSs on communication signals due to energy injected in ionosphere during the coupling mechanism between magnetosphere-ionosphere.
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Schmölter, Erik, Jens Berdermann, Norbert Jakowski, Christoph Jacobi, and Rajesh Vaishnav. "Delayed response of the ionosphere to solar EUV variability." Advances in Radio Science 16 (September 4, 2018): 149–55. http://dx.doi.org/10.5194/ars-16-149-2018.

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Abstract. Physical and chemical processes in the ionosphere are driven by complex interactions with the solar radiation. The ionospheric plasma is in particular sensitive to solar EUV and UV variations with a time delay between one and two days. This delay is assumed to be related to thermospheric transport processes from the lower ionosphere to the F region. In previous analyses, the delay has been investigated using the F10.7 index. Here we present preliminary results of the ionospheric delay based on a comprehensive and reliable database consisting of GNSS TEC Maps and EUV spectral flux data. We plan to specify the various dependencies from geographic/geomagnetic location, altitude, season, local time, geophysical and solar radiation conditions such as the solar activity level. The first results for dependencies from seasons and wavelengths regions of the EUV are presented in this paper. These results can provide more insight into ionospheric processes and are of interest for applications dependent on reliable ionospheric weather forecasts, e.g. GNSS error analyses, prediction and mitigation.
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Wilkinson, P. J. "Ionospheric variability and the international reference ionosphere." Advances in Space Research 34, no. 9 (2004): 1853–59. http://dx.doi.org/10.1016/j.asr.2004.08.007.

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Danzer, J., S. B. Healy, and I. D. Culverwell. "A simulation study with a new residual ionospheric error model for GPS radio occultation climatologies." Atmospheric Measurement Techniques 8, no. 8 (August 21, 2015): 3395–404. http://dx.doi.org/10.5194/amt-8-3395-2015.

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Abstract. In this study, a new model was explored which corrects for higher order ionospheric residuals in Global Positioning System (GPS) radio occultation (RO) data. Recently, the theoretical basis of this new "residual ionospheric error model" has been outlined (Healy and Culverwell, 2015). The method was tested in simulations with a one-dimensional model ionosphere. The proposed new model for computing the residual ionospheric error is the product of two factors, one of which expresses its variation from profile to profile and from time to time in terms of measurable quantities (the L1 and L2 bending angles), while the other describes the weak variation with altitude. A simple integral expression for the residual error (Vorob’ev and Krasil’nikova, 1994) has been shown to be in excellent numerical agreement with the exact value, for a simple Chapman layer ionosphere. In this case, the "altitudinal" element of the residual error varies (decreases) by no more than about 25 % between ~10 and ~100 km for physically reasonable Chapman layer parameters. For other simple model ionospheres the integral can be evaluated exactly, and results are in reasonable agreement with those of an equivalent Chapman layer. In this follow-up study the overall objective was to explore the validity of the new residual ionospheric error model for more detailed simulations, based on modeling through a complex three-dimensional ionosphere. The simulation study was set up, simulating day and night GPS RO profiles for the period of a solar cycle with and without an ionosphere. The residual ionospheric error was studied, the new error model was tested, and temporal and spatial variations of the model were investigated. The model performed well in the simulation study, capturing the temporal variability of the ionospheric residual. Although it was not possible, due to high noise of the simulated bending-angle profiles at mid- to high latitudes, to perform a thorough latitudinal investigation of the performance of the model, first positive and encouraging results were found at low latitudes. Furthermore, first application tests of the model on the data showed a reduction in temperature level of the ionospheric residual at 40 km from about −2.2 to −0.2 K.
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Danzer, J., S. B. Healy, and I. D. Culverwell. "A simulation study with a new residual ionospheric error model for GPS radio occultation climatologies." Atmospheric Measurement Techniques Discussions 8, no. 1 (January 27, 2015): 1151–76. http://dx.doi.org/10.5194/amtd-8-1151-2015.

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Abstract. In this study, a new model was explored, which corrects for higher order ionospheric residuals in global positioning system (GPS) radio occultation (RO) data. Recently, the theoretical basis of this new "residual ionospheric error model" has been outlined (Healy and Culverwell, 2015). The method was tested in simulations with a one-dimensional model ionosphere. The proposed new model for computing the residual ionospheric error is the product of two factors, one of which expresses its variation from profile-to-profile and from time-to-time in terms of measurable quantities (the L1 and L2 bending angles), the other of which describes the weak variation with altitude. A simple integral expression for the residual error (Vorob’ev and Krasil’nikova, 1994) has been shown to be in excellent numerical agreement with the exact value, for a simple Chapman layer ionosphere. In this case, the "altitudinal" element of the residual error varies (decreases) by no more than about 25% between ~10 and ~100 km for physically reasonable Chapman layer parameters. For other simple model ionospheres the integral can be evaluated exactly, and results are in reasonable agreement with those of an equivalent Chapman layer. In this follow-up study the overall objective was to explore the validity of the new residual ionospheric error model for more detailed simulations, based on modelling through a complex three-dimensional ionosphere. The simulation study was set up, simulating day and night GPS RO profiles for the period of a solar cycle with and without an ionosphere. The residual ionospheric error was studied, the new error model was tested, and temporal and spatial variations of the model were investigated. The model performed well in the simulation study, capturing the temporal variability of the ionospheric residual. Although, it was not possible, due to high noise of the simulated bending angle profiles at mid to high latitudes, to perform a thorough latitudinal investigation of the performance of the model, first positive and encouraging results were found at low latitudes. Furthermore, first application tests of the model on the data showed a reduction on temperature level of the ionospheric residual at 40 km from about −2.2 to −0.2 K.
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Parker, James A. D., S. Eleri Pryse, Natasha Jackson-Booth, and Rachel A. Buckland. "Modelling the main ionospheric trough using the Electron Density Assimilative Model (EDAM) with assimilated GPS TEC." Annales Geophysicae 36, no. 1 (January 25, 2018): 125–38. http://dx.doi.org/10.5194/angeo-36-125-2018.

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Abstract. The main ionospheric trough is a large-scale spatial depletion in the electron density distribution at the interface between the high- and mid-latitude ionosphere. In western Europe it appears in early evening, progresses equatorward during the night, and retreats rapidly poleward at dawn. It exhibits substantial day-to-day variability and under conditions of increased geomagnetic activity it moves progressively to lower latitudes. Steep gradients on the trough-walls on either side of the trough minimum, and their variability, can cause problems for radio applications. Numerous studies have sought to characterize and quantify the trough behaviour. The Electron Density Assimilative Model (EDAM) models the ionosphere on a global scale. It assimilates observations into a background ionosphere, the International Reference Ionosphere 2007 (IRI2007), to provide a full 3-D representation of the ionospheric plasma distribution at specified times and days. This current investigation studied the capability of EDAM to model the ionosphere in the region of the main trough. Total electron content (TEC) measurements from 46 GPS stations in western Europe from September to December 2002 were assimilated into EDAM to provide a model of the ionosphere in the trough region. Vertical electron content profiles through the model revealed the trough and the detail of its structure. Statistical results are presented of the latitude of the trough minimum, TEC at the minimum and of other defined parameters that characterize the trough structure. The results are compared with previous observations made with the Navy Ionospheric Monitoring System (NIMS), and reveal the potential of EDAM to model the large-scale structure of the ionosphere. Keywords. Ionosphere (midlatitude ionosphere; modelling and forecasting) – radio science (ionospheric physics)
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Yizengaw, Endawoke. "Global Longitudinal Dependence Observation of the Neutral Wind and Ionospheric Density Distribution." International Journal of Geophysics 2012 (2012): 1–11. http://dx.doi.org/10.1155/2012/342581.

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The statistical global view of the low-latitude ionospheric density stimulates further interest in studying the strong longitudinal variability of the ionospheric density structures in low-to-equatorial latitudes. However, we are not completely certain how the electrodynamics and ion-neutral coupling proceeds at low latitudes; in particular, the longitudinal difference in the dynamics of plasma structures in the low-to-mid latitude ionosphere is not yet fully understood. Numerical studies of latent heat release in the troposphere have indicated that the lower atmosphere can indeed introduce a longitudinal dependence and variability of the low-latitude ionosphere during quiet conditions. For the first time, we present simultaneous observations of the tidally modulated global wind structure, using TIDI observations, in the E-region and the ionospheric density distribution using ground (global GPS receivers) and space-based (C/NOFS in situ density and GPS TEC on CHAMP) instruments. Our results show that the longitudinally structured zonal wind component could be responsible for the formation of wave number four pattern of the equatorial anomaly.
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Zhang, S. R., and J. M. Holt. "Ionospheric climatology and variability from long-term and multiple incoherent scatter radar observations: variability." Annales Geophysicae 26, no. 6 (June 11, 2008): 1525–37. http://dx.doi.org/10.5194/angeo-26-1525-2008.

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Abstract. Long-term incoherent scatter radar (ISR) observations are used to study ionospheric variability for two midlatitude sites, Millstone Hill and St. Santin. This work is based on our prior efforts which resulted in an empirical model system, ISR Ionospheric Model (ISRIM), of climatology (and now variability) of the ionosphere. We assume that the variability can be expressed in three terms, the background, solar activity and geomagnetic activity components, each of which is a function of local time, season and height. So the background variability is ascribed mostly to the day-to-day variability arising from non solar and geomagnetic activity sources. (1) The background variability shows clear differences between the bottomside and the topside and changes with season. The Ne variability is low in the bottomside in summer, and high in the topside in winter and spring. The plasma temperature variability increases with height, and reaches a minimum in summer. Ti variability has a marked maximum in spring; at Millstone Hill it is twice as high as at St. Santin. (2) For enhanced solar activity conditions, the overall variability in Ne is reduced in the bottomside of the ionosphere and increases in the topside. For Te, the solar activity enhancement reduces the variability in seasons of high electron density (winter and equinox) at altitudes of high electron density (near the F2-peak). For Ti, however, while the variability tends to decrease at Millstone Hill (except for altitudes near 200 km), it increases at St. Santin for altitudes up to 350 km; the solar flux influence on the variability tends to be stronger at St. Santin than at Millstone Hill.
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Mielich, J., and J. Bremer. "A modified index for the description of the ionospheric short- and long-term activity." Annales Geophysicae 28, no. 12 (December 21, 2010): 2227–36. http://dx.doi.org/10.5194/angeo-28-2227-2010.

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Abstract. A modified ionospheric activity index AI has been developed on the basis of ionospheric foF2 observations. Such index can be helpful for an interested user to get information about the current state of the ionosphere. Using ionosonde data of the station Juliusruh (54.6° N; 13.4° E) this index has been tested for the time interval from January 1996 until December 2008. This index has no diurnal and seasonal variations, only a small positive dependence on the solar activity could be found. The variability of this index has, however, a marked seasonal variability with maxima during the equinoxes, a clear minimum in summer, and enhanced values in winter. The observed variability of AI is strongly correlated with the geomagnetic activity, most markedly during the equinoxes, whereas the influence of the solar activity is markedly smaller and mostly insignificant. Strong geomagnetic disturbances cause in middle latitudes in general negative disturbances in AI, mostly pronounced during equinoxes and summer and only partly during winter, thus in agreement with the current physical knowledge about ionospheric storms.
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Amabayo, Emirant Bertillas, Simon Katrini Anguma, and Edward Jurua. "Tracking the Ionospheric Response to the Solar Eclipse of November 03, 2013." International Journal of Atmospheric Sciences 2014 (October 23, 2014): 1–10. http://dx.doi.org/10.1155/2014/127859.

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The ionospheric dynamics is highly influenced by the solar radiation. During a solar eclipse, the moon occults the solar radiation from reaching the ionosphere, which may drastically affect the variability of the ionosphere. The variability of total electron content (TEC) observed by dual frequency Global Positioning System (GPS) receivers has made it possible to study effects of solar eclipse on the ionosphere. Total eclipse occurred on November 03, 2013, and the maximum amplitude was visible at Owiny in northern Uganda. Ionospheric behavior during this eclipse was analysed by using TEC data archived at Mbarara (MBAR), Malindi (MAL2), Eldoret (MOIU), and Kigali University (NURK) International GPS Satellite (IGS) stations. TEC variations of four consecutive days were used to study instantaneous changes of TEC during the eclipse event. The results generally show TEC decrease at the four stations. However, a maximum perturbation amplitude of ≥20 TECU was observed at MAL2 (18:00–20:00 UT) which is further south of the equator than the other stations. TEC enhancement and depletion were observed during the totality of the eclipse at MOIU, MBAR, NURK, and MAL2 (13:00–15:00 UT). This study found out that the ionospheric TEC over East Africa was modified by wave-like energy and momentum transport and obscuration of the solar disc due to the total solar eclipse.
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Dissertations / Theses on the topic "Ionospheric variability"

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Botai, Ondego Joel. "Ionospheric total electron content variability and its influence in radio astronomy." Thesis, Rhodes University, 2006. http://hdl.handle.net/10962/d1005258.

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Ionospheric phase delays of radio signals from Global Positioning System (GPS) satellites have been used to compute ionospheric Total Electron Content (TEC). An extended Chapman profle model is used to estimate the electron density profles and TEC. The Chapman profle that can be used to predict TEC over the mid-latitudes only applies during day time. To model night time TEC variability, a polynomial function is fitted to the night time peak electron density profles derived from the online International Reference Ionosphere (IRI) 2001. The observed and predicted TEC and its variability have been used to study ionospheric in°uence on Radio Astronomy in South Africa region. Di®erential phase delays of the radio signals from Radio Astronomy sources have been simulated using TEC. Using the simulated phase delays, the azimuth and declination o®sets of the radio sources have been estimated. Results indicate that, pointing errors of the order of miliarcseconds (mas) are likely if the ionospheric phase delays are not corrected for. These delays are not uniform and vary over a broad spectrum of timescales. This implies that fast frequency (referencing) switching, closure phases and fringe ¯tting schemes for ionospheric correction in astrometry are not the best option as they do not capture the real state of the ionosphere especially if the switching time is greater than the ionospheric TEC variability. However, advantage can be taken of the GPS satellite data available at intervals of a second from the GPS receiver network in South Africa to derive parameters which could be used to correct for the ionospheric delays. Furthermore GPS data can also be used to monitor the occurrence of scintillations, (which might corrupt radio signals) especially for the proposed, Square Kilometer Array (SKA) stations closer to the equatorial belt during magnetic storms and sub-storms. A 10 minute snapshot of GPS data recorded with the Hermanus [34:420 S, 19:220 E ] dual frequency receiver on 2003-04-11 did not show the occurrence of scintillations. This time scale is however too short and cannot be representative. Longer time scales; hours, days, seasons are needed to monitor the occurrence of scintillations.
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Codrescu, Mihail, Rajesh Vaishnav, Christoph Jacobi, Jens Berdermann, and E. Schmölter. "Ionospheric response to solar variability during solar cycles 23 and 24." Universität Leipzig, 2019. https://ul.qucosa.de/id/qucosa%3A74182.

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The ionospheric variability and its complexity is strongly dependent on continuous varying intense solar extreme ultraviolet (EUV) and UV radiations. We investigate the ionospheric response to the solar activity variations during the solar cycle (SC) 23 (1999-2008) and 24 (2009-2017) by using the F10.7 index, and Total Electron Content (TEC) maps provided by the international GNSS service (IGS). Wavelet cross-correlation method is used to evaluate the correlation between F10.7 and the global mean TEC. The maximum correlation is observed at the solar rotation time scale (16-32 days). There is a significant difference in the correlation at the time scale of 32-64 days. During SC 23, the correlation is stronger than during SC 24. This is probably due to the longer lifetime of active regions during SC 23. The wavelet variance estimation method suggests that the variance during SC 23 is more significant than during SC 24. Furthermore, the Coupled Thermosphere Ionosphere Plasmasphere Electrodynamics (CTIPe) model was used to reproduce the ionospheric delay of about 1-2 days observed in the IGS TEC observations. A strong correlation was modelled as well as observed during a high solar activity year (2013) as compared to low a solar activity year (2008).
Die ionosphärische Variabilität ist stark abhängig von der kontinuierlich variierenden intensiven solaren extrem ultravioletten (EUV) und UV-Strahlung. Wir untersuchen die ionosphärische Reaktion auf Variationen der Sonnenaktivität während der Sonnenzyklen (SC) 23 (1999-2008) und 24 (2009-2017) mit Hilfe des F10.7-Radioflussindexes und TEC (Gesamtelektronengehalt, Total Electron Content) -Karten, die vom internationalen GNSS-Dienst (IGS) bereitgestellt werden. Wavelet-Kreuzkorrelation wird verwendet, um die Korrelation zwischen F10.7 und global gemitteltem TEC zu bestimmen. Die maximale Korrelation wird auf der Zeitskala der Sonnenrotation (16-32 Tage) beobachtet. Es gibt einen signifikanten Unterschied in der Korrelation auf der Zeitskala von 32 bis 64 Tagen. Während des SC 23 ist die Korrelation stärker als während SC 24. Dies ist auf die längere Lebensdauer der aktiven Regionen zurückzuführen. Das Wavelet-Varianz-Schätzverfahren legt nahe, dass die Varianz beim SC 23 mehr von Bedeutung ist, als während SC 24. Des Weiteren wurde das gekoppelte Thermosphäre-Ionosphäre-Plasmasphäre-Elektrodynamik (CTIPe) Modell verwendet, um die ionosphärische Verzögerung von 1-2 Tagen zu reproduzieren. Eine starke Korrelation wurde bei hoher Sonnenaktivität (2013) im Gegensatz zu geringer Sonnenaktivität (2008) simuliert und auch beobachtet.
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Mbambo, Makhangela Casey. "Variability of the peak height of the ionospheric F2 layer over South Africa." Thesis, University of Fort Hare, 2011. http://hdl.handle.net/10353/446.

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Abstract This thesis will present an investigation into the variability of the maximum height of the ionospheric F2 layer, hmF2, with hour, season and latitude over the South African region. The dependence of hmF2 on solar and magnetic activity is also investigated. Data from three South African stations, namely Madimbo (22.4 S, 26.5 E), Grahamstown (33.3 S, 26.5 E) and Louisvale (28.5 S, 21.2 E) were used in this study. Initial results indicate that hmF2 shows a larger variability around midnight than during daytime for all the seasons. Monthly median values for hmF2 were used in all cases to illustrate the variability, and the International Reference Ionosphere (IRI) model has been used to investigate hmF2 predictability over South Africa. This research represents the initial steps towards a predictive model for the hmF2 parameter, with the long term aim of developing a new global hmF2 predictive model for the IRI. It is believed that this work will contribute signi cantly towards this aim through the understanding of the hmF2 parameter over a region that has not previously been investigated.
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Unglaub, C., Ch Jacobi, G. Schmidtke, B. Nikutowski, and R. Brunner. "EUV-TEC - an index to describe ionospheric variability using satellite-borne solar EUV measurements: first results." Universität Leipzig, 2010. https://ul.qucosa.de/id/qucosa%3A16362.

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Primary ionisation of major ionospheric constituents is calculated from satellite-borne solar EUV measurements. Number densities of the background atmosphere are taken from the NRLMSISE-00 climatology. From the calculated ionisation rates, an index termed EUV-TEC, which is based on the global total ionisation is calculated, and describes the ionospheric response to solar EUV and its variability. The index is compared against global mean ionospheric total electron content (TEC) derived from GPS data. Results show that the EUV-TEC index provides a better overall representation of global TEC than conventional solar indices like F10.7 do. The EUV-TEC index may be used for scientific research, and to describe the ionospheric effects on radio communication and navigation systems.
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McDonald, Sarah E. "Day to day and longitudinal variability of the nighttime low latitude terrestrial ionosphere." Fairfax, VA : George Mason University, 2007. http://hdl.handle.net/1920/2956.

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Thesis (Ph. D.)--George Mason University, 2007.
Title from PDF t.p. (viewed Jan. 21, 2008). Thesis director: Michael E. Summers, Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computational Sciences and Informatics. Vita: p. 204. Includes bibliographical references (p.193-203). Also available in print.
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Hui, Debrup. "Altitudinal Variability of Quiet-time Plasma Drifts in the Equatorial Ionosphere." DigitalCommons@USU, 2015. https://digitalcommons.usu.edu/etd/4536.

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The plasma drifts or electric fields and their structures in the ionosphere affect the accuracy of the present-day space-based systems. For the first time, we have used ionospheric plasma drift data from Jicamarca radar measurements to study the climatology of altitudinal variations of vertical and zonal plasma drifts in low latitudes during daytime. We used data from 1998 to 2014 to derive these climatological values in bimonthly bins from 150 km to 600 km. For the vertical plasma drifts, we observed the drifts increasing with altitudes in the morning and slowly changing to drifts decreasing with altitude in the afternoon hours. The drifts change mostly linearly from E- to F-region altitudes except in the morning hours of May-June when the gradients are very small. The zonal drifts show a highly nonlinear increase in the westward drifts at the lower altitudes and then increase slowly at the higher altitudes. We see a break in the slopes at lower altitudes during the morning hours of March-April and May-June. The E-region zonal drifts, unlike vertical drifts, show a very large variability compared to F-region drifts. We also explored the altitudinal profiles of vertical drifts during late afternoon and evening hours when the electrodynamic properties in the ionosphere change rapidly. For the first time using drifts up to 2000 km, we have shown the drifts increase and decrease below and above the F-region peak before becoming height independent. These structures arise to satisfy the curl-free condition of electric fields in low latitudes. The altitudinal gradients of vertical drifts are balanced by a time derivative of the zonal drifts to satisfy the curl-free condition of electric fields. We have shown how these structures evolve with local time around the dusk sector and change with solar flux. During solar minimum, the peak region can go well below 200 km. The present-day electric field models do not incorporate these gradients, particularly in the evening sectors when they change very rapidly. Very often their results do not match with the observations. Including these gradients along with proper magnetic field models will improve the model results and accuracy of our navigation, communication, and positioning systems.
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Ma, Qingjin. "Variability of the helium ion concentration in the topside ionosphere over Arecibo." Miami University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=miami1500286796832684.

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wang, xiaoni. "A STUDY OF EQUATORIAL IONOPSHERIC VARIABILITY USING SIGNAL PROCESSING TECHNIQUES." Doctoral diss., University of Central Florida, 2007. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2415.

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The dependence of equatorial ionosphere on solar irradiances and geomagnetic activity are studied in this dissertation using signal processing techniques. The statistical time series, digital signal processing and wavelet methods are applied to study the ionospheric variations. The ionospheric data used are the Total Electron Content (TEC) and the critical frequency of the F2 layer (foF2). Solar irradiance data are from recent satellites, the Student Nitric Oxide Explorer (SNOE) satellite and the Thermosphere Ionosphere Mesosphere Energetics Dynamics (TIMED) satellite. The Disturbance Storm-Time (Dst) index is used as a proxy of geomagnetic activity in the equatorial region. The results are summarized as follows. (1) In the short-term variations < 27-days, the previous three days solar irradiances have significant correlation with the present day ionospheric data using TEC, which may contribute 18% of the total variations in the TEC. The 3-day delay between solar irradiances and TEC suggests the effects of neutral densities on the ionosphere. The correlations between solar irradiances and TEC are significantly higher than those using the F10.7 flux, a conventional proxy for short wavelength band of solar irradiances. (2) For variations < 27 days, solar soft X-rays show similar or higher correlations with the ionosphere electron densities than the Extreme Ultraviolet (EUV). The correlations between solar irradiances and foF2 decrease from morning (0.5) to the afternoon (0.1). (3) Geomagnetic activity plays an important role in the ionosphere in short-term variations < 10 days. The average correlation between TEC and Dst is 0.4 at 2-3, 3-5, 5-9 and 9-11 day scales, which is higher than those between foF2 and Dst. The correlations between TEC and Dst increase from morning to afternoon. The moderate/quiet geomagnetic activity plays a distinct role in these short-term variations of the ionosphere (~0.3 correlation).
Ph.D.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Electrical Engineering PhD
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Chimidza, Oyapo. "The variability and predictability of the IRI shape parameters over Grahamstown, South Africa." Thesis, Rhodes University, 2008. http://hdl.handle.net/10962/d1005282.

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The International Reference Ionosphere (IRI) shape parameters B0, B1, and D1 provide a representation of the shape of the F2 layer, the thickness of the F2 layer and the shape of the F1 layer of the ionosphere respectively. The aim of this study was to examine the variability of these parameters using Grahamstown, South Africa (33.3±S, 26.5±E) ionosonde data and determine their predictability by the IRI-2001 model. A further aim of this study was to investigate developing an alternative model for predicting these parameters. These parameters can be determined from electron density profiles that are inverted from ionograms recorded with an ionosonde. Data representing the B0, B1 and D1 parameters, with half hourly or hourly intervals, were scaled and deduced from the digital pulse sounder (DPS) ionosonde for the period April 1996 to December 2006. An analysis of the diurnal, seasonal, and solar variations of the behaviour of these parameters was undertaken for the years 2000, 2004 and 2005 using monthly medians. Comparisons between the observational results and that of the IRI model (IRI 2001 version) indicate that the IRI-2001 model does not accurately represent the diurnal and seasonal variation of the parameters. A preliminary model was thus developed using the technique of Neural Networks (NNs). All available data from the Grahamstown ionosonde from 1996 to 2006 were used in the training of the NNs and the prediction of the variation of the shape parameters. Inputs to the model were the day number, the hour of day, the solar activity and the magnetic index. Comparisons between the preliminary NN model and the IRI-2001 model indicated that the preliminary model was more accurate at the prediction of the parameters than the IRI-2001 model. This analysis showed the need to improve the existing IRI model or develop a new model for the South African region. This thesis describes the results from this feasibility study which show the variability and predictability of the IRI shape parameters.
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Jacobi, Christoph, and Dierk Kürschner. "Interannual variability of the quasi two-day wave over Central Europe (52°N, 15°E)." Universitätsbibliothek Leipzig, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-223179.

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Using the spaced receiver method in the low-frequency (LF) range, lower E-region ionospheric drifts are measured at Collm Observatory, Germany since several decades. These drifts are interpreted as upper mesospheric winds at the reflection height of the used amplitude modulated LF radio waves, the latter being measured since 1983 using travel time differences between the ground wave and the ionospherically reflected sky wave within a small sideband range near 1.8 kHz above and below the carrier frequency. One regular feature of midlatitude upper mesosphere winds is the quasi twoday wave (QTDW), known as a wavenumber 3 or 4 wave in the middle atmosphere, usually occurring as one or more bursts during the summer season at midlatitudes. The OTDW bursts, as measured in LF winds, shows substantial decadal and interannual variability. Comparison with the background winds show that the onset of QDTW bursts is found near maximum values of the vertical wind shear, and maximum QTDW amplitudes are measured, on average, about one week after the maximum wind shear. This supports the theory that the QTDW is forced by instability of the summer mesospheric wind jet
Am Observatorium Collm werden seit mehreren Jahrzehnten Langwellenwindmessungen in der unteren ionosphärischen E-Schicht durchgeführt. Die zugehörige Reflexionshöhe wird, auf der Basis von Laufzeitdifferenzmessungen zwischen der Raum- und Bodenwelle, seit 1983 ebenfalls registriert. Eines der regelmäßig beobachteten Phänomene ist die quasi 2-Tage-Welle, die als eine planetare Welle der Wellenzahl 3 oder 4 bekannt ist. Diese Welle erscheint in mittleren Breiten in einem oder mehreren Schüben im Sommer. Nach den Messungen am Collm besitzt die Welle eine deutliche Variabilität von Jahr zu Jahr. Vergleiche mit dem zonalen Grundwind zeigen, dass das Auftreten von Maxima der 2-Tage-Welle in vielen Fällen mit erhöhter vertikaler Windscherung in Verbindung steht, so dass im langzeitlichen Mittel maximale Wellenamplituden einige Tage nach dem Auftreten maximaler Windscherung zu finden sind. Dies unterstützt die These, dass die quasi 2-Tage-Welle durch barokline Instabilität des sommerlichen Mesosphärenjets angeregt wird
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Books on the topic "Ionospheric variability"

1

W, Reinisch B., Bilitza D, McKinnell L. A, and URSI/COSPAR International Reference Ionosphere Working Group. Workshop, eds. IRI, quantifying ionospheric variability. Oxford: Published for the Committee on Space Research [by] Elsevier, 2004.

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R, Cander L., Kouris S, Zolesi B, and European Geophysical Society, eds. Ionospheric variability, modelling and predictions. Oxford: Pergamon, 2001.

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Advisory Group for Aerospace Research and Development. ElectromagneticWave Propagation Panel., ed. Ionospheric structure and variability on a global scale and interactio ns with atmosphere and magnetosphere: Papers presented at the Electromagnetic Wave Propagation Panel Symposium held in Munich, Germany, 16-20 May 1988. Neuilly sur Seine: Agard, 1989.

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Book chapters on the topic "Ionospheric variability"

1

Cander, Ljiljana R. "Ionospheric Variability." In Ionospheric Space Weather, 59–93. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99331-7_4.

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Somoye, Emmanuel O., Andrew O. Akala, Aghogho Ogwala, Eugene O. Onori, Rasaq A. Adeniji-Adele, and Enerst E. Iheonu. "Longitudinal Dependence of Day-to-Day Variability of Critical Frequency of Equatorial Type SporadicE(foEsq)." In Ionospheric Space Weather, 155–62. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781118929216.ch13.

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Koskinen, Hannu E. J., and Emilia K. J. Kilpua. "Particle Source and Loss Processes." In Astronomy and Astrophysics Library, 159–211. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82167-8_6.

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AbstractThe main sources of charged particles in the Earth’s inner magnetosphere are the Sun and the Earth’s ionosphere. Furthermore, the Galactic cosmic radiation is an important source of protons in the inner radiation belt, and roughly every 13 years, when the Earth and Jupiter are connected via the interplanetary magnetic field, a small number of electrons originating from the magnetosphere of Jupiter are observed in the near-Earth space. The energies of solar wind and ionospheric plasma particles are much smaller than the particle energies in radiation belts. A major scientific task is to understand the transport and acceleration processes leading to the observed populations up to relativistic energies. Equally important is to understand the losses of the charged particles. The great variability of the outer electron belt is a manifestation of the continuously changing balance between source and loss mechanisms, whereas the inner belt is much more stable.
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Liu, H. L. "WACCM-X Simulation of Tidal and Planetary Wave Variability in the Upper Atmosphere." In Modeling the Ionosphere-Thermosphere System, 181–99. Chichester, UK: John Wiley & Sons, Ltd, 2014. http://dx.doi.org/10.1002/9781118704417.ch16.

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Smith, Anne K., Daniel R. Marsh, Martin G. Mlynczak, James M. Russell, and Jeffrey C. Mast. "SABER Observations of Daytime Atomic Oxygen and Ozone Variability in the Mesosphere." In Aeronomy of the Earth's Atmosphere and Ionosphere, 75–82. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-0326-1_5.

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Krall, Jonathan, Joseph D. Huba, Douglas P. Drob, Geoff Crowley, and Richard E. Denton. "Day-to-Day Variability of the Quiet-Time Plasmasphere Caused by Thermosphere Winds." In Magnetosphere-Ionosphere Coupling in the Solar System, 235–41. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119066880.ch19.

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Natras, Randa, Dzana Halilovic, Medžida Mulić, and Michael Schmidt. "Mid-latitude Ionosphere Variability (2013–2016), and Space Weather Impact on VTEC and Precise Point Positioning." In Advanced Technologies, Systems, and Applications VII, 471–91. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17697-5_37.

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Mendillo, Michael. "Day-to-day variability of the ionosphere." In The Dynamical Ionosphere, 7–11. Elsevier, 2020. http://dx.doi.org/10.1016/b978-0-12-814782-5.00002-9.

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Lapenta, Giovanni. "Space weather: Variability in the Sun-Earth connection." In The Dynamical Ionosphere, 61–85. Elsevier, 2020. http://dx.doi.org/10.1016/b978-0-12-814782-5.00008-x.

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Nebdi, Hamid. "Space Weather and Link to Climate Change." In Advances in Environmental Engineering and Green Technologies, 1–20. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7775-1.ch001.

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Our nearest shining star, the Sun, source of radiations and energy, sometimes generates severe events and phenomena in space which can affect our technology and biosphere. On the other hand, space weather, as defined by National Aeronautics and Space Administration (NASA), is conditions on the Sun and in the solar wind, magnetosphere, ionosphere, and thermosphere that can influence the performance and reliability of space-borne and ground-based technological systems and can endanger human life or health. A brief description of the Sun-Earth connection is firstly presented. Secondly, a particular attention is given to highlight the Sun's variability and the link between the space weather and climate change by means of some recent studies.
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Conference papers on the topic "Ionospheric variability"

1

Zhang, Qingbo, Ze Yu, and Peng Xiao. "Impacts of ionospheric temporal variability on L-band GEO SAR imaging." In IGARSS 2016 - 2016 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2016. http://dx.doi.org/10.1109/igarss.2016.7729302.

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Furman, W. N., and J. W. Nieto. "The effects of channel variability on high data rate HF communications." In 10th IET International Conference on Ionospheric Radio Systems and Techniques (IRST 2006). IEE, 2006. http://dx.doi.org/10.1049/cp:20060247.

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Darya, Abdollah Masoud, Muhammad Mubasshir Shaikh, and Ilias Fernini. "Longitudinal Variability Study of Ionospheric Ranging Errors Around 20 N Geomagnetic Latitude." In 2019 6th International Conference on Space Science and Communication (IconSpace). IEEE, 2019. http://dx.doi.org/10.1109/iconspace.2019.8905928.

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Bertot, Edward, Stephen Lynch, and CDR Dan Lubin. "Using GPS TEC measurements to model ionospheric variability with local oceanic tidal modes." In 2015 USNC-URSI Radio Science Meeting (Joint with AP-S Symposium). IEEE, 2015. http://dx.doi.org/10.1109/usnc-ursi.2015.7303563.

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Medvedeva, Irina V., Konstantin Ratovsky, and Maxim Tolstikov. "Year-to-year changes in atmospheric and ionospheric variability in the 24th solar cycle." In 28th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics, edited by Oleg A. Romanovskii and Gennadii G. Matvienko. SPIE, 2022. http://dx.doi.org/10.1117/12.2644623.

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de la Vega, D., S. Lopez, I. Angulo, U. Gil, I. Pena, D. Guerra, P. Angueira, and J. L. Ordiales. "Statistical characterization of the field strength location variability for the new digital radio services in the medium wave band." In IET 11th International Conference on Ionospheric Radio Systems and Techniques (IRST 2009). IEE, 2009. http://dx.doi.org/10.1049/cp.2009.0053.

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Candido, C. M. N., I. S. Batista, F. Becker-Guedes, V. Klausner, and P. M. S. Negreti. "Low latitude ionospheric variability during solar minimum 2008: Impact of Solar Wind High Speed Streams." In 14th International Congress of the Brazilian Geophysical Society & EXPOGEF, Rio de Janeiro, Brazil, 3-6 August 2015. Brazilian Geophysical Society, 2015. http://dx.doi.org/10.1190/sbgf2015-306.

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Kozlovtseva, E. A., N. A. Tereshin, A. M. Padokhin, and G. A. Kurbatov. "Study on Seasonal Variability of Ionospheric TEC Estimated Using Signals of Compass/BeiDou Geostationary Satellites." In 2018 Progress in Electromagnetics Research Symposium (PIERS-Toyama). IEEE, 2018. http://dx.doi.org/10.23919/piers.2018.8597665.

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Yasyukevich, A. S., M. A. Chernigovskaya, A. A. Mylnikova, B. G. Shpynev, and D. S. Khabituev. "Seasonal and helio-geomagnetic activity pattern of the ionospheric variability over Russia's Eastern Siberia and Far East region from the GPS/GLONASS data." In 2017 Progress In Electromagnetics Research Symposium - Spring (PIERS). IEEE, 2017. http://dx.doi.org/10.1109/piers.2017.8262081.

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Zhang, Shun-Rong, John M. Holt, Paul Song, John Foster, Michael Mendillo, and Dieter Bilitza. "Climatology and Variability of the Ionosphere: Incoherent Scatter Radar Observations." In RADIO SOUNDING AND PLASMA PHYSICS. AIP, 2008. http://dx.doi.org/10.1063/1.2885035.

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Reports on the topic "Ionospheric variability"

1

Paul, Adolf K. Ionospheric Variability. Fort Belvoir, VA: Defense Technical Information Center, March 1989. http://dx.doi.org/10.21236/ada209251.

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Sprague, R. A. Measurements of Ionospheric Variability. Fort Belvoir, VA: Defense Technical Information Center, October 1994. http://dx.doi.org/10.21236/ada293495.

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Sprague, R. A. Spatial Correlation of Ionospheric Variability. Fort Belvoir, VA: Defense Technical Information Center, March 1994. http://dx.doi.org/10.21236/ada278105.

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Mendillo, Michael. Multiple Characteristics of Ionospheric Variability Patterns. Fort Belvoir, VA: Defense Technical Information Center, September 2006. http://dx.doi.org/10.21236/ada612083.

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Mendillo, Michael. Multiple Characteristics of Ionospheric Variability Patterns. Fort Belvoir, VA: Defense Technical Information Center, September 2007. http://dx.doi.org/10.21236/ada573273.

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Houminer, Zwi. Investigation of the Ionospheric Short-Term Variability. Fort Belvoir, VA: Defense Technical Information Center, October 1994. http://dx.doi.org/10.21236/ada286253.

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Forbes, Jeffrey M. Vertical Coupling and Variability in the Tropical Atmosphere/Ionosphere System. Fort Belvoir, VA: Defense Technical Information Center, March 2002. http://dx.doi.org/10.21236/ada402521.

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8

Shellman, C. H. Variability of the Electric Field Strength in the Earth-Ionosphere Waveguide Due to Variations in the Electron Density Profile. Fort Belvoir, VA: Defense Technical Information Center, December 1992. http://dx.doi.org/10.21236/ada264808.

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