Title: Distributions-oriented wind forecast verification by a hidden Markov model for multivariate circular-linear data,
Abstract: Winds from the North-West quadrant and lack of precipitation are known to
lead to an increase of PM10 concentrations over a residential neighborhood in
the city of Taranto (Italy). In 2012 the local government prescribed a
reduction of industrial emissions by 10% every time such meteorological
conditions are forecasted 72 hours in advance. Wind forecasting is addressed
using the Weather Research and Forecasting (WRF) atmospheric simulation system
by the Regional Environmental Protection Agency. In the context of
distributions-oriented forecast verification, we propose a comprehensive
model-based inferential approach to investigate the ability of the WRF system
to forecast the local wind speed and direction allowing different performances
for unknown weather regimes. Ground-observed and WRF-forecasted wind speed and
direction at a relevant location are jointly modeled as a 4-dimensional time
series with an unknown finite number of states characterized by homogeneous
distributional behavior. The proposed model relies on a mixture of joint
projected and skew normal distributions with time-dependent states, where the
temporal evolution of the state membership follows a first order Markov
process. Parameter estimates, including the number of states, are obtained by a
Bayesian MCMC-based method. Results provide useful insights on the performance
of WRF forecasts in relation to different combinations of wind speed and
direction.