What is the validity of using a kernel density estimation to compare model x observed data?
In other words, if the KDE curve for the observed data looks like the KDE for the model forecast, can I use this result as a quality measure for the model? If not, why?
In this case, it's a weather forecast model - but I have some complex variables like wind direction, for which a simple correlation will not help too much. For example, the wind going from 360 to 5 is a huge numeric decrease, but it's almost the same direction!