Toolbox for Evaluating Ensembles Using an Information Gain Measure
Using our library, we found that the standard deviation of the climatology is a better measure of forecast uncertainty than the ensemble spread. These results led us to implement a 2-3 month lookback auto-regressive climatological model to show that forecast provides more information then recent information. We then incorporated forecast into this vectorized auto-regressive model, finding that combining climatology, recent history, and forecast did better than and individual element.
The scientific Python stack was integral in our work. The simplicity of NumPy, SciPy, and Matplotlib made for a relatively quick turnaround from idea to code and the use of IPython notebooks greatly sped up the write, run, review results cycle.