# Kalman filter and Box-Cox

I'm interested in wind forecasting, which I have analyzed over some time by means of ARMA methods. Now I've being reading about Kalman filtering. Kalman filter is optimal when Gaussian assumption can be hold. However, wind distributions are far from normal (with Weibull the most popular). My question is, Is it correct to transform a wind speed time series by means of Box-Cox transformation before estimating the parameters of Kalman filter, so that the normality assumption holds? If it were so, why then using particle filters?