A study of the magnetosphere-ionosphere coupling processes

Thesis (Ph.D.) University of Alaska Fairbanks, 1990Magnetosphere-ionosphere (M-I) coupling processes are studied by using numerical modeling. An M-I coupling model of substorms on the ionospheric recombination time scale (tens of seconds) is developed. The model is two-dimensional (2-D) and time-dependent from which several signatures of substorms can be obtained and understood. The model is then extended to northward interplanetary magnetic field (IMF) conditions to study the effects of the M-I coupling on the high-latitude convection. Based on the model results, a mechanism for the origin of distorted two-cell ionospheric convection is proposed. The ionospheric and ground signatures of multiple field-aligned current sheets originating from dayside flux transfer events have been modeled. The interaction between Alfven waves and field aligned potential drops is studied by using a local model

Estimation and testing for spatially indexed curves with application to ionospheric and magnetic field trends

We develop methodology for the estimation of the functional mean and the functional principal components when the functions form a spatial process. The data consist of curves $X(\mathbf{s}_k;t),t\in[0,T],$ observed at spatial locations $\mathbf{s}_1,\mathbf{s}_2,...,\mathbf{s}_N$. We propose several methods, and evaluate them by means of a simulation study. Next, we develop a significance test for the correlation of two such functional spatial fields. After validating the finite sample performance of this test by means of a simulation study, we apply it to determine if there is correlation between long-term trends in the so-called critical ionospheric frequency and decadal changes in the direction of the internal magnetic field of the Earth. The test provides conclusive evidence for correlation, thus solving a long-standing space physics conjecture. This conclusion is not apparent if the spatial dependence of the curves is neglected.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS524 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Field-Aligned Current Associated with a Distorted Two-Cell Convection Pattern During Northward Interplanetary Magnetic Field

A systematic study of the influence of the ionospheric conductance on the field-aligned current associated with a distorted two-cell convection pattern during northward interplanetary magnetic field (IMF) has been conducted. Our modeling results indicate that the NBZ current can be associated with the distorted two-cell convection for most of the ionospheric conductivity conditions. It is shown that the conductivity conditions related to the seasonal variation and the aurora activity can significantly influence the basic features of the field-aligned current associated with a distorted two-cell convection pattern. It is found that the increase of the field-aligned current in the polar cap during the summer is mainly due to the increasing contribution from the Pedersen current, and the increase of the field-aligned current in both the oval region and the evening-midnight sector during the active aurora period is mainly due to the increasing contribution from the Hall current. It is also found that the conductivity change related to the solar variation has its impact mainly on the intensity of a field-aligned current system instead of its pattern. On the basis of the modeling results it is suggested that the field-aligned current system observed by the Magsat satellite (Iijima and Shibaji, 1987) might imply a distorted two-cell convection pattern, and a four-cell convection pattern is most likely to occur when the direction of the IMF is due north or very close to the north

Probability Tails of Wavelet Coefficients of Magnetometer Records

The ground-based magnetometer network has long been a powerful tool for monitoring and observing the variations of the currents flowing in the magnetosphere-ionosphere (M-I) system. The time series of magnetograms are nonstationary and their frequency behavior changes over time. They are therefore not amenable to traditional time domain or spectral (Fourier) analysis. In recent years, various new mathematical techniques have been developed to analyze magnetometer data and the wavelet technique has stood out as being particularly relevant. In order to correctly make statistical inferences based on wavelet analysis, the wavelet coefficient distributions of magnetograms must be examined. In this work, we apply the discrete wavelet transform to the 1-min magnetometer records and then use several statistical techniques to analyze the probability distributions of the wavelet coefficients. It is found that the distributions of these coefficients for both storm and quiet times are highly nonnormal and can be classified as being heavy tailed. This finding suggests that when applying statistical techniques to the wavelet coefficients of the magnetograms, one must make sure that these techniques are robust to large departures from Gaussianity manifested by the presence of heavy probability tails. It is also found that the tail indexes for storm times are on average smaller than those of quiet times, which reflects the stronger impulsive and nonstationary features in magnetometer data during storm times, and the shifts are most significant for the wavelet coefficients corresponding to physical scales of 4–8 min

Effect of High Latitude Ionospheric Convection on Sun-Aligned Polar Caps

A coupled magnetospheric-ionospheric (M-I) MHD model has been used to simulate the formation of Sun-aligned polar cap arcs for a variety of interplanetary magnetic field (IMF) dependent polar cap convection fields. The formation process involves launching an Alfvén shear wave from the magnetosphere to the ionosphere where the ionospheric conductance can react self-consistently to changes in the upward currents. We assume that the initial Alfvén shear wave is the result of solar wind-magnetosphere interactions. The simulations show how the E region density is affected by the changes in the electron precipitation that are associated with the upward currents. These changes in conductance lead to both a modified Alfvén wave reflection at the ionosphere and the generation of secondary Alfvén waves in the ionosphere. The ensuing bouncing of the Alfvén waves between the ionosphere and magnetosphere is followed until an asymptotic solution is obtained. At the magnetosphere the Alfvén waves reflect at a fixed boundary. The coupled M-I Sun-aligned polar cap arc model of Zhu et al. (1993a) is used to carry out the simulations. This study focuses on the dependence of the polar cap arc formation on the background (global) convection pattern. Since the polar cap arcs occur for northward and strong By IMF conditions, a variety of background convection patterns can exist when the arcs are present. The study shows that polar cap arcs can be formed for all these convection patterns; however, the arc features are dramatically different for the different patterns. For weak sunward convection a relatively confined single pair of current sheets is associated with the imposed Alfvén shear wave structure. However, when the electric field exceeds a threshold, the arc structure intensifies, and the conductance increases as does the local Joule heating rate. These increases are faster than a linear dependence on the background electric field strength. Furthermore, above the threshold, the single current sheet pair splits into multiple current sheet pairs. For the fixed initial ionospheric and magnetospheric conditions used in this study, the separation distance between the current pairs was found to be almost independent of the background electric field strength. For either three-cell or distorted two-cell background convection patterns the arc formation favored the positive By case in the northern hemisphere

Uncertainty Associated with Modeling the Global Ionosphere

A study has been conducted of the effect that different physical assumptions have on global models of the electron density distribution. The study was conducted with the Ionosphere Forecast Model (IFM) and the Ionosphere Plasmasphere Model (IPM) developed by Utah State University. Both physics-based, time-dependent, global models use the same empirical models for the neutral atmosphere (MSIS) and neutral wind (Horizontal Wind Model, HWM), but the altitude range, thermal structure, number of ion species, and magnetic 2ield are different. The IFM covers the altitude range from 90-1400 km, calculates the densities for four ions (NO+, O2+, N2+, O+), has a simple prescription for calculating H+, and is based on a tilted offset dipole magnetic 2ield. The IPM extends from 90-20,000 km, includes six ion species (NO+, O2+, N2+, O+, H+, He+), is based on the International Geomagnetic Reference Field (IGRF), and allows for inter-hemisphere 2low. Therefore, the comparison of these models will elucidate the quantitative effect of these differences. In addition, simulations were conducted to study the effect of uncertainties in the zonal wind, secondary electron production, O+/ O collision frequency, tidal structure, and state of plasmasphere re2illing. The simulations were conducted for a wide range of solar, seasonal, and geomagnetic activity levels. Quantitative results will be given that establish the importance of the various physical processes

Towards Open-Scenario Semi-supervised Medical Image Classification

Semi-supervised learning (SSL) has attracted much attention since it reduces the expensive costs of collecting adequate well-labeled training data, especially for deep learning methods. However, traditional SSL is built upon an assumption that labeled and unlabeled data should be from the same distribution e.g., classes and domains. However, in practical scenarios, unlabeled data would be from unseen classes or unseen domains, and it is still challenging to exploit them by existing SSL methods. Therefore, in this paper, we proposed a unified framework to leverage these unseen unlabeled data for open-scenario semi-supervised medical image classification. We first design a novel scoring mechanism, called dual-path outliers estimation, to identify samples from unseen classes. Meanwhile, to extract unseen-domain samples, we then apply an effective variational autoencoder (VAE) pre-training. After that, we conduct domain adaptation to fully exploit the value of the detected unseen-domain samples to boost semi-supervised training. We evaluated our proposed framework on dermatology and ophthalmology tasks. Extensive experiments demonstrate our model can achieve superior classification performance in various medical SSL scenarios