Physical models use physics based simulations to predict wind paths.

Think PV=nRT, Navier Stokes, Finite Element Analysis-type stuff. A mix of fundamental physical equations and simplified engineering math based on historical data.

Different simulations use different assumptions in the underlying models so they give different output.

The catch is there's often orders of magnitude more variables at play than physical models are designed to utilize, which is where machine learning comes in super handy - drawing statistical inferences from millions of data points to provide a forecast based on statistics, not physics.