Interesting post.

Just a few suggestions:

NNs works better, as someone said, with real numbers not with integers. NNs is a non-linear interpolation so the result will be spreaded in the range for that variable you want to predice.

If you force the result to integer or worst to binary, you lost the richness of the prediction and obtain ambigous data. In the case you've presented the NN can give you back the PoP value in the range 0% and 100% exactly as a professional weather forecasting system could do.

So NNs are not well suitable, due their intrinsic nature, for discrete systems.

The input data are the most important thing, NNs learn only from them and if the information are not representive enough of the system, you will never obtain good results.

In other words the input data have to be wide enough to cover all the situation in that the system could work.

If your NN gives you bad results you should look at the input data as the first thing, and ask if the data give to the NN all the info it needs as quantity of different "situations" and if some input was not lost.

When you try to model real system as you did, the input problem is often challenging and is "the problem".

For example I think that the data of the region are not enough and data from neighboring regions could be have an influence on the next day's Pop (because the clouds are moving).

Or again, 5 years of data is a too small set.

But your approach is interesting and SQL server could be an useful to prototype a NN prediction system and then when well tested, develop an external software to achieve better performance.

Giuseppe