Weather is often given the chaos theory treatment, where it's considered so sensitive to the initial conditions that sparked its movement -- and may be influenced in drastic ways by small changes -- that an approximation of what may come can be completely and utterly wrong.
Of course, that doesn't mean we stop trying to predict it. And we're getting better, but we're still far from certain and unable to give anything remotely accurate over a long period of time. But big data analytics may be about to change that.
Spearheading the trend is a company called Weather Analytics, which plans to use traditional metrics like barometric pressure, fluid dynamics, and numerical weather prediction together with big data to create a much more accurate model for long-term forecasting.
Traditionally, forecasts grow less accurate as the timeframe moves beyond a month or two. Weather Analytics believes that it can make accurate-enough reports on the scale of years, and that probability indexes can be created to help clients weigh the possible costs of weather-related interference in their long-term plans.
The data Weather Analytics uses to generate its predictions comes from over 33 years of weather reports from across the globe, as well as ongoing data from many countries today. The company site says it uses "more than 50 times the global historical data reported by any other source or provider."
Of course, all models affected by chaos theory are destined to be unpredictable, and it seems unlikely that Weather Analytics will be able to create a system that is wholly accurate, but company founder Bill Pardue told Forbes recently that he is convinced they can do better than what's currently available.
Is it raining where you are?
In looking for global weather databases before the venture even began, Pardue and his partners spoke to many organizations that claimed to have extensive records, but none did. Most repositories were limited to weather patterns in North America and Western Europe, he told Forbes. Meanwhile, MIT research meteorologist John Keller had been archiving and scrubbing global weather data in his spare time at home. His database formed the foundation of the new company.
The combination of historical records and real-time feeds should give Weather Analytics an edge, and if you consider its client base, it's already off and running. In less than a year of operation, Forbes reports, Weather Analytics is inundated with client requests, many of which involve weather pattern data in places where information has been scarce. One client was interested in weather effects on African cotton crops, for example, and another wanted to find out how weather in the British Virgin Islands might affect a 24-month construction project.
No doubt, Weather Analytics will also do what every other big data firm does and rent out portions of its data for other analytics efforts.
What do you think, members? Will big data finally enable accurate long-term weather prediction? Would this kind of service be useful to you? Share your thoughts below.