  Methodical aerial population surveys monitoring critically endangered species
in Canadian North Atlantic waters are instrumental in influencing government
policies both in economic and conservational efforts. The primary factor
hindering the success of these missions is poor visibility caused by glare.
This paper builds off our foundational paper [1] and pushes the envelope toward
a data-driven glare modelling system. Said data-driven system makes use of
meteorological and astronomical data to assist aircraft in navigating in order
to mitigate acquisition errors and optimize the quality of acquired data. It is
found that reliably extracting aircraft orientation is critical to our
approach, to that end, we present a GPS alignment methodology which makes use
of the fusion of two GPS signals. Using the complementary strengths and
weaknesses of these two signals a synthetic interpolation of fused data is used
to generate more reliable flight tracks, substantially improving glare
modelling. This methodology could be applied to any other applications with
similar signal restrictions.
