Whether it’s raining, snowing or sunny where you’re sitting, you can be sure that data analytics, predictive modeling or some combination of business intelligence tools are part of the weather forecasts. Visualization is a fancy name for the radar and other maps at 6 and 11p.m. that can show you the conditions down to the street corner details.
Accurate or not, a forecast is still an ‘educated guess’ even when backed by modeling, analytics, extensive histories and other tools. Results can always turn out differently than you expect.
There’s more at stake than knowing whether to pack an umbrella tomorrow. National Public Radio cited a Commerce Department estimate that weather affects as much as one-third of the U.S. Gross Domestic Product. So it makes dollars and sense to use modeling, business intelligence tools, and analytics to predict patterns or develop forecasts that are as detailed and accurate as possible.
Airlines and utilities use forecasts and analytics to plan daily operations. Farming, travel and countless other decisions every day rely on detailed weather predictions. And that has spawned entire new industries from climate-based insurance, trading of futures contracts and a never-ending quest for specifics on everything from humidity to temperature, snowfall, wind and sun brightness. Those factors have never been as important as when deciding where to locate wind, solar and other renewable power sources.
The real treasure is in the past – data that when analyzed thoroughly can provide a panorama of details on everything from temperature, wind direction and speed, rain or humidity and other particulars. That’s why John Keller doesn’t complain about the weather, he profits from it, operating Weather Analytics Inc. a Winchester, MA company that compiles detailed reports and projections on climate conditions and models. Other examples include Accu-Weather, the Weather Channel and most local television and radio stations have invested millions on maps, modeling and other analytics tools.
IBM is developing a technique to boost weather forecasting using big data. This article http://asmarterplanet.com/blog/2013/02/23603.html gives an idea of the attempts taken the IBM researchers in weather forecasting by using data analytics.
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