Simulations have demonstrated that high-energy neutrinos (E > 1017 eV) are detected cost-efficiently via the Askaryan effect in ice, where a particle cascade induced by the neutrino interaction produces coherent radio emission that can be picked up by antennas installed below the surface. A good knowledge of the near surface ice (aka firn) properties is required to reconstruct the neutrino properties. In particular, a continuous monitoring of the snow accumulation (which changes the depth of the antennas) and the index-of-refraction profile are crucial for an accurate determination of the neutrino's direction and energy. 14 months of data of the ARIANNA detector on the Ross Ice Shelf, Antarctica, are presented where a prototype calibration system was successfully used to monitor the snow accumulation with unprecedented precision of 1 mm. Several algorithms to extract the time differences of direct and reflected (off the surface) signals (D'n'R time difference) from noisy data (including deep learning) are explored. This constitutes an in-situ test of the neutrino vertex distance reconstruction using the D'n'R technique which is needed to determine the neutrino energy. Additionally, an in-situ calibration system is proposed that extends the radio detector station with a radio emitter to continuously monitor the firn properties by measuring D'n'R time difference. In a simulation study the station layout is optimized and the achievable precision is quantified.

High-energy neutrino physics offers a unique way to investigate the most violent phenomena in our universe. The detection of energies above E > 1017 eV is most efficient using the Askaryan effect, where a neutrino-induced particle shower produces coherent radio emission that is detectable with radio antennas. By using radio techniques large volumes can be covered with few stations at moderate cost exploiting the large attenuation length of radio in cold ice. Key to the reconstruction of the neutrino properties visa precise and continuous monitoring of the firn properties. In particular the snow accumulation (changing the absolute depth of the antennas thus the propagation path of the signal) and the index-of-refraction profile are crucial for the neutrino energy and direction reconstruction. This work presents an in-situ calibration design that acts as an detector extension by adding additional emitter antennas to the station design to continuously monitor the firn properties by measuring the direct and reflected signals (D’n’R). In a simulation study the optimal station layout is determined and the achievable precision is quantified. Furthermore 14 months of data from an ARIANNA station at the Ross Ice Shelf, Antarctica, are presented where a prototype of this calibration system has been successfully installed to monitor the snow accumulation with unprecedented precision of 1 mm. Several algorithms, including deep learning algorithms, to compute the D’n’R time difference from radio traces are considered.