There is a nifty new paper (in PDF format) that describes how you can fruitfully use simple time series U.S. weather data to make in money in weather markets:
We take a simple time-series approach to modeling and forecasting daily average temperature in U.S. cities, and we inquire systematically as to whether it may prove useful from the vantage point of participants in the weather derivatives market. The answer is, perhaps surprisingly, yes. Time-series modeling reveals conditional mean dynamics, and crucially, strong conditional variance dynamics, in daily average temperature, and it reveals sharp differences between the distribution of temperature and the distribution of temperature surprises. As we argue, it also holds promise for producing the long-horizon predictive densities crucial for pricing weather derivatives, so that additional inquiry into time-series weather forecasting methods will likely prove useful in weather derivatives contexts.
This is just another example of how new sources of data being exposed by the Internet and elsewhere are creating new investment opportunities. The proof: People are having such a hard time keeping up that you can exploit simple time-series data in a liquid market.