  In this paper we develop a white noise framework for the study of stochastic
partial differential equations driven by a d-parameter (pure jump) Levy white
noise. As an example we use this theory to solve the stochastic Poisson
equation with respect to Levy white noise for any dimension d. The solution is
a stochastic distribution process given explicitly. We also show that if d\leq
3, then this solution can be represented as a classical random field in L2(\mu
), where \mu is the probability law of the Levy process.
  The starting point of our theory is a chaos expansion in terms of generalized
  Charlier polynomials. Based on this expansion we define Kondratiev spaces and
the Levy Hermite transform.
