First off, I looked a little bit on wikis to find https://en.wikipedia.org/wiki/Multivariate_interpolation which might have what you want but I am not sure. However, the way I wanted to do it is with fmincon. I attached the objective function I created. Basically it creates a function of the form
then it loads in the X,Y, and wind data that you have and determines how closely it fits by a basic difference and summing.
This is utilized by fmincon as such:
a = fmincon(@objfun, rand(6,1), eye(6),ones(6,1).*20)
I decided to make the initial conditions random just because. Then, because this is a constrained minimum search, I just made it so that no value could go over 20, it would also likely work with large constraints as well but I doubt they will come into affect much. I tried using other optimization algorithms that did not require constraints but they did not work as well. For the example you gave the output should be
It generally gets pretty close, sometimes the constant value messes up though.