  The inversion of a Levy measure was first introduced (under a different name)
in Sato 2007. We generalize the definition and give some properties. We then
use inversions to derive a relationship between weak convergence of a Levy
process to an infinite variance stable distribution when time approaches zero
and weak convergence of a different Levy process as time approaches infinity.
This allows us to get self contained conditions for a Levy process to converge
to an infinite variance stable distribution as time approaches zero. We
formulate our results both for general Levy processes and for the important
class of tempered stable Levy processes. For this latter class, we give
detailed results in terms of their Rosinski measures.
