The electrodynamics of the Earth's low-latitude ionosphere is dependent on the ionospheric conductivity and the thermospheric neutral density, temperature, and winds present. This two-part study focused on the gravity wave seeding mechanism of equatorial plasma depletions in the ionosphere and the associated equatorial spread F, as well as the differences between a two-dimensional flux tube integrated electrodynamics model and a three-dimensional model for the same time period. The gravity wave seeding study was based on a parameterization of a gravity wave perturbation using a background empirical thermosphere and a physics-based ionosphere for the case of 12 UT on 26 September 2002. The electrodynamics study utilized a two-dimensional flux tube integrated model in center dipole coordinates, which is derived in this work. This case study examined the relative influence of the zonal wind, meridional wind, vertical wind, temperature, and density perturbations of the gravity wave. It further looked at the angle of the wave front to the field line flux tube, the most influential height of the perturbation, and the difference between planar and thunderstorm source gravity waves with cylindrical symmetry. The results indicate that, of the five perturbation components studied, the zonal wind is the most important mechanism to seed the Rayleigh-Taylor instability needed to develop plasma plumes. It also shows that the bottomside of the F-region is the most important region to perturb, but a substantial E-region influence is also seen. Furthermore, a wave front with a small angle from the field line is necessary, but the shape of the wave front is not critical in the gravity wave is well developed before nightfall. Preliminary results from the three-dimensional model indicate that the equipotential field line assumption of the two-dimensional model is not valid below 100 km and possibly higher. Future work with this model should attempt to examine more of the differences with the two-dimensional model in the electric fields and currents produced as well as with the plasma drifts that lead to plume development.

Research over the years has established that the Gradient Drift Instability process causes small-scale irregularities, mostly along the edges of the high-latitude polar cap patches. Studying these irregularities will help in the development of a global Scale Ionospheric model, which is a central part of a global space weather forecast system. Much theoretical work has been done with varying degrees of complexity to study this instability in the high latitude patches. In this work we have used the Utah State University Time Dependent Ionospheric Model to model the high-latitude patches, calculate the growth rate of the instability, and perform a macro-scale study of the phenomenon. This is the first time that real ionospheric values have been used to calculate the growth rate and to provide two-dimensional maps identifying Gradient Drift Instability-caused irregularity regions in the polar cap. Our research shows that regions of intense instability occur along the edges of the tongue of ionization and its throat regions with strong rates along the borders of the cusp region.

The chemical reaction of the first excited electronic state of molecular nitrogen, N₂(A), with ground state atomic oxygen is an important contributor to thermospheric nitric oxide (NO).  The importance is assessed by including this reaction in a one-dimensional photochemical model.  The method is to scale the photoelectron impact ionization rate of molecular nitrogen by a Gaussian centered near 100 km. Large uncertainties remain in the temperature dependence and branching ratios of many reactions important to NO production and loss. Similarly large uncertainties are present in the solar soft x-ray irradiance, known to be the fundamental driver of the low-latitude NO.  To illustrate, it is shown that the equatorial, midday NO density measured by the Student Nitric Oxide Explorer (SNOE) satellite near the Solar Cycle 23 maximum can be recovered by the model to within the 20% measurement uncertainties using two rather different but equally reasonable chemical schemes, each with their own solar soft-xray irradiance parameterizations.  Including the N₂(A) changes the NO production rate by an average of 11%, but the NO density changes by a much larger 44%.  This is explained by tracing the direct, indirect, and catalytic contributions of N₂(A) to NO, finding them to contribute 40%, 33%, and 27% respectively. The contribution of N₂(A) relative to the total NO production and loss is assessed by tracing both back to their origins in the primary photoabsorption and photoelectron impact processes.  The photoelectron impact ionization of N₂ is shown to be the main driver of the midday NO production while the photoelectron impact dissociation of N₂ is the main NO destroyer.  The net photoelectron impact excitation rate of N₂, which is responsible for the N₂(A) production, is larger than the ionization and dissociation rates and thus potentially very important.   Although the conservative assumptions regarding the level-specific NO yield from the N₂(A)+O reaction results in N₂(A) being a somewhat minor contributor, N₂(A) production is found to be the most efficient producer of NO among the thermospheric energy deposition processes. / Ph. D.

京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第20183号 / 理博第4268号 / 新制||理||1613(附属図書館) / 京都大学大学院理学研究科地球惑星科学専攻 / (主査)教授 家森 俊彦, 教授 田口 聡, 教授 塩谷 雅人 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM

The BIOMASS mission from the European Space Agency (ESA) is designed to measurebiomass and carbon content in Earth’s forests. To account for phase changes caused byionospheric variations, a map-drift autofocus algorithm is developed, which utilises a phasescreen of the ionosphere to eliminate phase errors in the signal. In this development, a filteris employed to integrate and remove noise from the second-order derivative of the ionosphericphase screen. This thesis aims to analyse methods to implement this filter andcompare their efficiency. Two filters are constructed using two methods, a Least Mean Square (LMS) filter and aWiener filter. Further emphasis is placed on the Wiener filter, and the most optimal way tocalculate it is explored in detail. The aim is to produce a filter that can integrate, lower theimpact of noise as much as possible and be computationally efficient. An implementationwas made in Python using simulated data of an ionosphere. The conclusion is that the Wiener filter can yield improved results if a precise estimation ofthe autocorrelation function of the ionospheric phase screen can be determined, and thatlinear regression models might be a method to do so. There is also consideration taken tothe noise of the data, it is compensated for by utilising multiple data sources. Additionally,to enhance computational efficiency, a comparison of different solving methods for the linearsystem of equations that is the filter where made, showing a LU-decomposition method tobe efficient.

Earth's atmosphere is a dynamic region with a complex interplay of energetic inputs, outputs, and transport mechanisms. A complete understanding of the atmosphere and how various fields within it interact is essential for predicting atmospheric shifts relevant for spaceflight, the evolution of Earth's climate, radio communications, and other practical applications. In-situ observations of a critical altitude region within Earth's atmosphere from 100-200 km in altitude, a subset of a larger 90 – 400 km altitude region deemed the "Thermospheric Gap", are required for constraining atmospheric models of wind, temperature, and density perturbations caused by atmospheric tides. Observations within this region that are sufficient to fully reconstruct and understand the evolution of tides therein are nonexistent. Certain missions have sought to fill portions of this observation gap, including Daedalus which was selected as a candidate for the Earth Explorer program by the European Space Agency in 2018. This study focuses on the design and optimization of a two-satellite, highly elliptical satellite constellation to perform in-situ observations and reconstruction of tidal features in the 100-200 km region. The model atmosphere for retrieving sample data is composed of DE3 and DE2 tidal features from the Climatological Model of the Thermosphere (CTMT) and background winds from the Thermosphere-Ionosphere-Electrodynamic General Circulation Model (TIEGCM). BoTorch, a Bayesian Optimization package for Python, is integrated with the Ansys Systems Tool Kit (STK) to model the constellation's propagation and simulated atmospheric sampling. A least squares fitting algorithm is utilized to fit the sampled data to a known tidal function form. Key results include 14 Pareto optimal solutions for the satellite constellation based on a set of 7 objective functions, 3 constellation input parameters, and a sample set of n = 86. Four of these solutions are discussed in more detail. The first two are the best and second-best options on the Pareto front for sampling and reconstruction of the input tidal fields. The third is the best solution for latitudinal tidal fitting coverage. The fourth is a compromise solution that nearly minimizes delta-v expenditure, while sacrificing some quality in tidal fitting and fitting coverage. / Master of Science / Earth's atmosphere, the envelope of gaseous material surrounding the planet from an altitude of 0 km to approximately 10,000 km, is a dynamic system with a diverse set of energy inputs, outputs, and transfer mechanisms. A complete understanding of the atmosphere and how various fields within it interact is essential for predicting atmospheric shifts relevant for spaceflight, the evolution of Earth's climate, radio communications, and other practical applications. The atmosphere life breathes on Earth's surface evolves in physical and chemical properties, such as temperature, pressure, and composition, as distance from Earth increases. In addition, the atmosphere varies temporally, with shifts in its properties occurring on several timescales, some as short as a few minutes and some on the order of the age of the planet itself. This thesis project seeks to study the optimization of a satellite system to further understand an important source of atmospheric variability – atmospheric tides. Just as the forces of gravity from the moon and sun cause tides in the oceans, the Earth's rotation and the periodic absorption of heat into the atmosphere from the sun cause atmospheric tides. A model atmosphere with a few tides and a background wind is generated to perform simulated tidal sampling. The latitude, longitude, and altitude coordinates of the satellites as they propagate through the atmosphere are used to model samples of the northward and southward atmospheric winds and determine how well the constellation does at regenerating the input tidal data. The integration of several software tools and a Bayesian Optimization algorithm automate the process of finding a range of options for the constellation to best perform the tidal fitting, minimize satellite fuel consumption, and cover as many latitude bands of the atmosphere as possible.

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A few years ago, the charge-transfer reaction CO2+  +  O ⟶ O+ + CO2 was investigated experimentally for the first time since the study by Fehsenfeld et al., (1970). This new investigation was conducted by Tenewitz et al., (2018). The rate coefficient k < 6 × 10−13 cm3 s−1 , concluded by Tenewitz et al. (2018), differed substantially from the value of 9.6 × 10−11 cm3 s−1 reported by Fehsenfeld et al., (1970). Fox et al., (2021) showed that the old rate constants for the two channels of the CO2+  +  O interaction work much better than the new ones in reproducing chemical features of the Martian ionosphere. Here, we combine MAVEN/NGIMS and TIMED/SEE data to conduct model-observation comparisons of O+ concentrations in the dayside Martian ionosphere. We consider each orbit of the MAVEN Deep Dip 2 (DD2) campaign between 17-22 April 2015. In the model, we balance the production rate of O+ through the aforementioned charge transfer reaction and the photoionisation of O and CO2 , with the loss rate through the reaction O+ + CO2 ⟶ O2+ + CO. We find a better level of agreement between the modelled and the observed O+ concentrations— (i) towards closest approach altitudes (∼ 130 km), and (ii) when using the old rate constant instead of the new one for the charge transfer reaction CO2+ +  O ⟶ O+ + CO2 .

There was a large radar campaign during 2007 and 2008, the International Polar Year (IPY),and at that time the EISCAT Svalbard Radar was operated and measured the ionosphere continuouslyat most times. This report presents statistical results from an electron enhancementpoint of view. Until now there has been some research into the field and results based on theions in the ionosphere, and the enhancements we refer to as Naturally enhanced ion acousticlines (NEIALs). Plasma line data from May 2007 to February 2008 has been analysed inorder to find and classify enhancements as NEIALs have been classified but with respect tothe electron distribution instead of the ion distribution. A method of detection was developedin order to differentiate the enhancements from the background with a relation between theminimum and maximum power of each measured dump. Results show that there is a largedifference between the downshifted plasma lines and the upshifted plasma lines, both has arange distribution peak at 180 km and the upshifted plasma line has another peak at 230 kmwhich the downshifted plasma line does not. The occurrence rate of the enhancements was1.64 % for the downshifted plasma line and 4.69 % for the upshifted plasma line. Threedifferent types of enhancements are classified using the variance distribution for the peakfrequency of that detected dump, Single, Profile, and Diffuse. The Single enhancements havea bit different spectral, range, and time of day distributions than of the Profile and Diffusedistributions. The Diffuse classifications are mostly wrong classifications and aliasing and itis very similar to Profile enhancements as seen by its distribution.

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