A multivariate circular-linear hidden Markov model and site-specific assessment of wind predictions by an atmospheric simulation system

Winds from the North-West quadrant and lack of precipitation are known to lead to an increase of PM10 concentrations in the city of Taranto. In 2012 the Apulia Government prescribed a reduction of industrial emissions by 10% every time such meteorological conditions are forecasted 72 hours in advance. Wind prediction is addressed using the Weather Research and Forecasting (WRF) atmospheric simulation system by the Regional Environmental Protection Agency (ARPA Puglia). We investigate the ability of the WRF system to properly predict the local wind speed and direction allowing different performances for unknown weather regimes. Observed and WRF-predicted wind speed and direction at a relevant location are jointly modeled as a 4-dimensional time series with a finite number of states (wind regimes) characterized by homogeneous distributional behavior. Observed and simulated wind data are made of two circular (direction) and two linear (speed) variables, then the 4-dimensional time series is jointly modeled by a mixture of projected-skew normal distributions with time-dependent states, where the temporal evolution of the state membership follows a first order Markov process. Parameter estimates are obtained by a Bayesian MCMC-based method and results provide useful insights on wind regimes corresponding to different performances of WRF predictions.