Pybrain Reinforcement Learning dynamic output
It is certainly possible to train a neural network (based on pybrain or otherwise) to make predictions of this sort that are better than a coin toss.
However, weather prediction is a very complex art, even for people who do it as their full-time profession and have been for decades.  Those weather forecasters have much bigger neural networks inside their head than pybrain can simulate.  If it were possible to make accurate predictions in the way you describe, then it would have been done long ago.  For this reason, I  wouldn't expect to do better than (or even as well as) the local weather forecaster.  So, if your goal is to learn pybrain, I would pick a less complex system to model, and if your goal is to predict the weather, I would suggest www.wunderground.com.
Can you use Reinforcement Learning from Pybrain on dynamic changing output. For example weather: lets say you have 2 attributes Humidity and Wind and the output will be either Rain or NO_Rain ( and all attributes are either going to have a 1 for true or 0 for false in the text file i am using). can you use Reinforcement Learning on this type of problem? the reason I ask is sometime even if we have humidity it does not guaranty that its going to rain.