  Interoperability issues concerning observational data have gained attention
in recent times. Automated data integration is important when it comes to the
scientific analysis of observational data from different sources. However, it
is hampered by various data interoperability issues. We focus exclusively on
semantic interoperability issues for observational characteristics. We propose
a use-case-driven approach to identify general classes of interoperability
issues. In this paper, this is exemplarily done for the use-case of citizen
science fireball observations. We derive key concepts for the identified
interoperability issues that are generalizable to observational data in other
fields of science. These key concepts contain several modeling challenges, and
we broadly describe each modeling challenges associated with its
interoperability issue. We believe, that addressing these challenges with a set
of ontology design patterns will be an effective means for unified semantic
modeling, paving the way for a unified approach for resolving interoperability
issues in observational data. We demonstrate this with one design pattern,
highlighting the importance and need for ontology design patterns for
observational data, and leave the remaining patterns to future work. Our paper
thus describes interoperability issues along with modeling challenges as a
starting point for developing a set of extensible and reusable design patterns.
