  The transition from non-renewable to renewable energies represents a global
societal challenge, and developing a sustainable energy portfolio is an
especially daunting task for developing countries where little to no
information is available regarding the abundance of renewable resources such as
wind. Weather model simulations are key to obtain such information when
observational data are scarce and sparse over a country as large and
geographically diverse as Saudi Arabia. However, output from such models is
uncertain, as it depends on inputs such as the parametrization of the physical
processes and the spatial resolution of the simulated domain. In such
situations, a sensitivity analysis must be performed and the input may have a
spatially heterogeneous influence of wind. In this work, we propose a latent
Gaussian functional analysis of variance (ANOVA) model that relies on a
nonstationary Gaussian Markov random field approximation of a continuous latent
process. The proposed approach is able to capture the local sensitivity of
Gaussian and non-Gaussian wind characteristics such as speed and threshold
exceedances over a large simulation domain, and a continuous underlying process
also allows us to assess the effect of different spatial resolutions. Our
results indicate that (1) the non-local planetary boundary layer scheme and
high spatial resolution are both instrumental in capturing wind speed and
energy (especially over complex mountainous terrain), and (2) the impact of
planetary boundary layer scheme and resolution on Saudi Arabia's planned wind
farms is small (at most 1.4%). Thus, our results lend support for the
construction of these wind farms in the next decade.

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