530 Physik
Refine
Document Type
- Article (1)
- Doctoral Thesis (1)
Language
- English (2) (remove)
Is part of the Bibliography
- yes (2)
Keywords
- ionosphere (2) (remove)
Institute
Near-Earth space represents a significant scientific and technological challenge. Particularly at magnetic low-latitudes, the horizontal magnetic field geometry at the dip equator and its closed field-lines support the existence of a distinct electric current system, abrupt electric field variations and the development of plasma irregularities. Of particular interest are small-scale irregularities associated with equatorial plasma depletions (EPDs). They are responsible for the disruption of trans-ionospheric radio waves used for navigation, communication, and Earth observation. The fast increase of satellite missions makes it imperative to study the near-Earth space, especially the phenomena known to harm space technology or disrupt their signals. EPDs correspond to the large-scale structure (i.e., tens to hundreds of kilometers) of topside F region irregularities commonly known as Spread F. They are observed as depleted-plasma density channels aligned with the ambient magnetic field in the post-sunset low-latitude ionosphere. Although the climatological variability of their occurrence in terms of season, longitude, local time and solar flux is well-known, their day to day variability is not. The sparse observations from ground-based instruments like radars and the few simultaneous measurements of ionospheric parameters by space-based instruments have left gaps in the knowledge of EPDs essential to comprehend their variability.
In this dissertation, I profited from the unique observations of the ESA’s Swarm constellation mission launched in November 2013 to tackle three issues that revealed novel and significant results on the current knowledge of EPDs. I used Swarm’s measurements of the electron density, magnetic, and electric fields to answer, (1.) what is the direction of propagation of the electromagnetic energy associated with EPDs?, (2.) what are the spatial and temporal characteristics of the electric currents (field-aligned and diamagnetic currents) related to EPDs, i.e., seasonal/geographical, and local time dependencies?, and (3.) under what conditions does the balance between magnetic and plasma pressure across EPDs occur?
The results indicate that: (1.) The electromagnetic energy associated with EPDs presents a preference for interhemispheric flows; that is, the related Poynting flux directs from one magnetic hemisphere to the other and varies with longitude and season. (2.) The field-aligned currents at the edges of EPDs are interhemispheric. They generally close in the hemisphere with the highest Pedersen conductance. Such hemispherical preference presents a seasonal/longitudinal dependence. The diamagnetic currents increase or decrease the magnetic pressure inside EPDs. These two effects rely on variations of the plasma temperature inside the EPDs that depend on longitude and local time. (3.) EPDs present lower or higher plasma pressure than the ambient. For low-pressure EPDs the plasma pressure gradients are mostly dominated by variations of the plasma density so that variations of the temperature are negligible. High-pressure EPDs suggest significant temperature variations with magnitudes of approximately twice the ambient. Since their occurrence is more frequent in the vicinity of the South Atlantic magnetic anomaly, such high temperatures are suggested to be due to particle precipitation.
In a broader context, this dissertation shows how dedicated satellite missions with high-resolution capabilities improve the specification of the low-latitude ionospheric electrodynamics and expand knowledge on EPDs which is valuable for current and future communication, navigation, and Earth-observing missions. The contributions of this investigation represent several ’firsts’ in the study of EPDs: (1.) The first observational evidence of interhemispheric electromagnetic energy flux and field-aligned currents. (2.) The first spatial and temporal characterization of EPDs based on their associated field-aligned and diamagnetic currents. (3.) The first evidence of high plasma pressure in regions of depleted plasma density in the ionosphere. These findings provide new insights that promise to advance our current knowledge of not only EPDs but the low-latitude post-sunset ionosphere environment.
Accurately predicting total electron content (TEC) during geomagnetic storms is still a challenging task for ionospheric models. In this work, a neural-network (NN)-based model is proposed which predicts relative TEC with respect to the preceding 27-day median TEC, during storm time for the European region (with longitudes 30 degrees W-50 degrees E and latitudes 32.5 degrees N-70 degrees N). The 27-day median TEC (referred to as median TEC), latitude, longitude, universal time, storm time, solar radio flux index F10.7, global storm index SYM-H and geomagnetic activity index Hp30 are used as inputs and the output of the network is the relative TEC. The relative TEC can be converted to the actual TEC knowing the median TEC. The median TEC is calculated at each grid point over the European region considering data from the last 27 days before the storm using global ionosphere maps (GIMs) from international GNSS service (IGS) sources. A storm event is defined when the storm time disturbance index Dst drops below 50 nanotesla. The model was trained with storm-time relative TEC data from the time period of 1998 until 2019 (2015 is excluded) and contains 365 storms. Unseen storm data from 33 storm events during 2015 and 2020 were used to test the model. The UQRG GIMs were used because of their high temporal resolution (15 min) compared to other products from different analysis centers. The NN-based model predictions show the seasonal behavior of the storms including positive and negative storm phases during winter and summer, respectively, and show a mixture of both phases during equinoxes. The model's performance was also compared with the Neustrelitz TEC model (NTCM) and the NN-based quiet-time TEC model, both developed at the German Aerospace Agency (DLR). The storm model has a root mean squared error (RMSE) of 3.38 TEC units (TECU), which is an improvement by 1.87 TECU compared to the NTCM, where an RMSE of 5.25 TECU was found. This improvement corresponds to a performance increase by 35.6%. The storm-time model outperforms the quiet-time model by 1.34 TECU, which corresponds to a performance increase by 28.4% from 4.72 to 3.38 TECU. The quiet-time model was trained with Carrington averaged TEC and, therefore, is ideal to be used as an input instead of the GIM derived 27-day median. We found an improvement by 0.8 TECU which corresponds to a performance increase by 17% from 4.72 to 3.92 TECU for the storm-time model using the quiet-time-model predicted TEC as an input compared to solely using the quiet-time model.