Weather Data - Part II : The reader

So how do we extract cloud data? One method is simply by observation. However, why observe when we have the satellites to help us.

The data

The data can be found here although I currently see that the server is down (hopefully this will change soon). 

The data format

The data format is sort of summarized in this document here.

The reader

The reader written in C can be found here. However, beware, this code only works for a big endian machine. For little endian, make this substitution:
data.Preptr->npix becomes ntohl(data.Preptr->npix)


The reader (Python version)

At a recent hackathon in Montreal, I took the liberty to rewrite the library in python just to help with the visibility of this data, and also well, learn python.
Here is the source.

I would like to point out that there is also a java version written by Andre Mas here. We wrote the library in parallel just to challenge ourselves and better understand this data at a lower level.

Anyway, so what's the result?
Here is a quick result on the infrared radiance.

data set: ISCCP.DX.0.GOE-7.1991.01.01.0000 the infrared radiance measured.

Why did I choose the radiance? Because infrared is visible during the day or night. (For those who are not familiar with infrared, click this wiki link. It is like light, but longer wavelength and radiation we can't see with our eyes. But your camera can see it.) Anyway, so the satellite data also does contain data showing what the sky would look like with our eyes (visible spectrum), but then I would be limited to data that was taken during the local daytime of each place measured.


So now we have a database that can tell us the cloudiness of a certain region. Will be be able to do anything more with this? We'll see