A multivariate circular-linear hidden Markov model for distributions-oriented wind forecast verication

Winds from the North-West quadrant and lack of precipitation are known to lead to an increase of PM10 concentrations in a residential neighborhood of the city of Taranto (Apulia, 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 prediction is addressed using the Weather Research and Forecasting (WRF) atmospheric simulation system by the Regional Environmental Protection Agency (ARPA Puglia). In the framework of distributions-oriented forecast verication, we investigate the ability of the WRF system to properly predict the local wind speed and direction allowing dierent performances for unknown wind regimes. Ground-observed and WRF-predicted wind speed and direction at a relevant location are jointly modeled as a 4-dimensional time series with a nite 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-independent states, where the temporal evolution of the state membership follows a rst order Markov process. Parameter estimates are obtained by a Bayesian MCMCbased method and results provide useful insights on wind regimes corresponding to dierent performances of WRF predictions.