Jonathan wrote:
"Hmm no idea how monte carlo simulations has anything at all to do with temperature analysis"
They do and they are important. Google for:
"Climate" and "Monte Carlo Simulation"
The first link I find is:
http://physics.gac.edu/~huber/envision/instruct/montecar.htm
which contains the following:
Now, for the problem we are studying, namely determining the global climate, there are several places where a Monte Carlo simulation can be of use. In particular, we will need it to help us determine the global average temperature and the amount of sunlight which falls into each latitude band. To determine the global average temperature, we want to average of the temperature of each latitude band, but there is obviously much more land area in the region from the equator to a latitude of 10o than in a band from 80o to the north pole. Therefore to determine the average temperature, we will want to weight the temperature of each band by the fraction of the earths land area in that band. We can do this analytically using integration, but this also can be done well with a Monte Carlo method.
We will modify the program above to generate random (XYZ) points in a cube of sides 1 unit. Next we will determine if they are on the surface of a sphere of radius 1 by using the following:
Rrand = Xrand.^2 + Yrand.^2 + Zrand.^2;
CheckValue = Rrand<=1.01 & Rrand>=.99;
this will determine if the points are on the surface of the sphere. Next, we will check if points are within each latitude band as well as on the surface of the sphere. The program will increment a counter for each point which meets these criteria. At the end, we can divide the number in each latitude band by the total number of points which were on the surface to find the area in each band.
Like I said above. It is not the data I don't trust ... it is the statistical methodology and the conclusions.
