  We introduce a new representation of the rotating shallow water equations
based on a stochastic transport principle. The derivation relies on a
decomposition of the fluid flow into a large-scale component and a noise term
that models the unresolved small-scale flow. The total energy of such a random
model is demonstrated to be preserved along time for any realization. To
preserve this structure, we combine an energy (in space) preserving
discretization of the underlying deterministic model with approximations of the
stochastic terms that are based on standard finite volume/difference operators.
This way, our method can directly be used in existing dynamical cores of global
numerical weather prediction and climate models. For an inviscid test case on
the f-plane we use a homogenous noise and illustrate that the spatial part of
the stochastic scheme preserves the total energy of the system. Moreover, using
an inhomogenous noise, we show for a barotropically unstable jet on the sphere
that the proposed random model better captures the structure of a large-scale
flow than a comparable deterministic model.
