Positioning systems, such as GPS or Galileo, play a vital role in geodetic sciences, since their observations are routinely used to define
the frame upon which global scale Earth observing missions are referred to. Additionally, the proper physical description of the involved
satellite orbits allows us to indirectly determine geophysical properties of the Earth (for example, its orientation at a microarcsecond level).
This is only possible due to the ongoing effort that we dedicate to the development of novel processing algorithms and cutting-edge physical models.
Probes exploring the solar system are regularly communicating with the Earth. Their trajectory can be reconstructed thanks to, e.g., the Doppler effect (frequency shift).
This PhD thesis focuses on Europa and Callisto, two of Jupiter's moons. Considering probes in orbit around them, we estimate their geodetic parameters,
such as their gravity fields, from the perturbations they induce on the probe's orbit.
Knowing more about these parameters can help us better constrain the internal structure models of Europa and Callisto, such as the density and the composition
of their internal layers e.g., the existence of a subsurface liquid ocean on Europa.
Solar flares release huge amounts of energy and can eject material into space towards Earth.
Thereby, the electronics of satellites or airplanes can be disturbed or even large-scale power failures can occur.
We analyze observations of the Sun utilizing machine learning to build prediction models and find the configurations
of the solar atmosphere that lead to an eruption. The Sun is still not well understood, learning more about it is of both
scientific importance and practical value to better protect our infrastructure against threats from space.
The standard procedure is to jointly process GNSS satellite and ground station data to determine the orbits of the GNSS satellites and the precise coordinates of the ground stations, such as the one in Zimmerwald.
The orbits of low Earth orbiting satellites (LEOs) are computed separately, assuming that the GNSS satellites' trajectories are correct. In my work, however, all three elements
are jointly processed, and additional geodetic parameters, such as the Earth’s center of mass, are calculated. The goal of my work is to determine whether, and to what extent,
the derived parameters improve when all data are processed together.
The Sun offers a unique opportunity: it provides spatially resolved data - an advantage rarely encountered when studying remote celestial bodies.
Of particular importance are "solar flares", due to their effects on Earth’s atmosphere and technology.
While prior efforts have made notable progress in understanding individual flares, investigating the statistical characteristics of hundreds of such flares still remains.
The integration of Machine Learning (ML) into the field of solar physics presents a unique solution.
However, the task is not just to predict when solar flares will happen, but also to understand the physics of solar flares.
This renders the application of ML to Solar Physics both challenging and inherently rewarding.
Satellite Laser Ranging (SLR) is a geodetic technique, which measures the time-of-flight of ultra-short laser pulses emitted
from a ground station to an Earth orbiting satellite and reflected back to the ground station. Ideally, the satellites
are spherical, small, heavy, and equipped with reflectors. Based on investigating these measurements, one can draw
conclusions about changes in the system Earth, e.g., in the lengths of a day, the position of the Earth's axis of rotation or mass re-distributions.
In my thesis, I analysed specific problems involving the enhancement of optical observation techniques to pave the way
towards a sustainable use of the outer space. One challenge comprised the development of a new method for being able to
use the laser ranging observation technique with inaccurate ephemerides or even after the discovery of a new resident space object.
Other challenges comprised the observation during daylight, and the fusion of observables to gain more information about the dynamics
of the observed targets. Julian's thesis
A key to understanding the dynamic system Earth is the continuous observation of its time-variable gravity field,
which allows for conclusions about mass movements, typically water transport on and near the Earth's surface. As a consequence,
these measurements map climate change. The gravity field is monitored by dedicated satellite missions, e.g., the Gravity Recovery And Climate Experiment (GRACE)
and its successor GRACE Follow-on, which ultra precisely measure distance changes between a pair of Earth orbiting satellites separated by a few hundred kilometres.
These observations lay the foundation for modelling of the Earth’s time-variable gravity field, typically on a basis of monthly snapshots. Image credit: NASA.