Ultra high energy neutrinos can be detected by measurement of radio emission, produced either
from Askaryan emission, or via reflection of an in-ice radar transmission off the neutrino’s
ionization trail. Accurate reconstruction of the properties depend on accurately modeling radio
propagation through the firn layer, the transition between fresh snow and glacial ice, where the
refractive index is inhomogeneous over depth and range.
The paraPropPython code uses the parabolic equation (PE) simulation methods for im-
proved modeling of RF transmission in polar firn. PE methods permit simulation of arbitrary RF
waveforms through data-driven depth and range dependent refractive index profiles on a scale of
several kilometres, accounting for features such as surface roughness, crevasses and refrozen-ice
layers, and can also model back-scatter off of in-ice anomalies. This work aims to examine
the effects of ice inhomogeneities on signal properties using the parabolic wave equation (PE)
method of simulation radio propagation, using the upper firn layer of the Greenland ice sheet at
the Summit station as an example. We also present improvements and updates to paraPropPython,
and show an inversion method to reconstruct refractive index profiles from ice-penetrating radar
data.