  We consider the problem of short-term forecasting of surface wind speed
probability distribution. Our approach consists in predicting the parameters of
a given probability density function by training a neural network model whose
loss function is the corresponding log-likelihood. We compare different
possibilities among a set of distributions that have been previously considered
in the context of modeling wind fluctuations. Our results rely on two wind
datasets recorded respectively by M\'et\'eo-France in Corsica (South France)
and by KNMI over the Netherlands. A first part of our work globally unveils the
superiority of the so-called "Multifractal Rice" (M-Rice) distribution over
alternative parametric models, showcasing its potential as a reliable tool for
wind speed forecasting. This family ofdistributions relies on a random cascade
model model for wind speeds along the same picture as fully developed
turbulence. For all stations in both regions, it consistently provides better
results regardless of the considered scoring rule or forecasting horizon. Our
second findings demonstrate significant enhancements in forecasting accuracy
when one incorporates wind speed data from proximate weather stations, in full
agreement with former results for point-wise wind speed prediction. Moreover,
we reveal that the incorporation of ERA5 reanalysis of 10 m wind data from
neighboring grid points contributes to a substantial improvement mostly at
longest time horizons ($h=6$ h). It turns out that accounting for pertinent
explanatory factors, notably those related the spatial distribution and wind
speed and direction, emerges as a more critical factor in enhancing accuracy
than the choice of the "optimal" parametric distribution. We also find out that
such explanatory factors mainly increases the resolution performances while it
does not change the reliability contribution to the CRPS score.
