The Weather Company, an IBM (NYSE: IBM) Business, announced today they will leverage one of the largest Internet of Things (IoT) platforms in the world to provide critical weather data to millions of people in currently underserved areas. With severe weather statistically occurring more frequently, governments and businesses are seeking supplemental weather data to better prepare for impending disasters. For the period 2010-2015, severe weather events caused more than 100 billion in damage in the U.S., where modern warning systems exist, according to the National Oceanic and Atmospheric Administration. Even more concerning is the fact that 70 developing countries lack robust early warning systems, exposing citizens to potentially life-threatening disasters. On a global scale, The World Bank reports that over the past 30 years, natural disasters, which include severe weather, have taken an estimated 2.5 million lives and cost more than US 4 trillion.
It was a tale of two storms. The first consisted of the rain and thunder forecast for Bournemouth by the BBC weather app on the Saturday spring bank holiday. The second came when the first failed to materialise and a tourism manager in the town complained that visitors who stayed away could have come after all and enjoyed sunshine and blue skies. This opportunity to rage at inaccurate forecasting, bash the BBC and highlight the grievances of small businesses did not go to waste. For the Sun, it was a "blunderstorm".
This story was originally published by Grist and appears here as part of the Climate Desk collaboration. In an era of increasingly extreme hurricanes, floods, and drought, the people in charge of preparing for disasters depend on meteorologists to anticipate where the next catastrophe might strike. Here's some good news: Meteorologists are coming through, with unprecedented accuracy. In fact, weather forecasting technology has improved so dramatically that humanitarian organizations can now fund disaster relief before disaster hits. It's a revolutionary change that could save countless lives.
Make fun of the weatherman if you want but modern forecasts have quietly, by degrees, become much better. Meteorologists are now as good with their five-day forecasts as they were with their three-day forecasts in 2005. Both government and private weather forecasting companies are approaching the point where they get tomorrow's high temperature right nearly 80 percent of the time. In this Wednesday, Oct. 31, 2012 file photo, waves wash over a roller coaster from a Seaside Heights, N.J., amusement park that fell in the Atlantic Ocean during Superstorm Sandy. Both government and private weather forecasting companies are approaching the point where they get tomorrow's high temperature right nearly 80 percent of the time.
Traditional models of weather forecasting are based on statistical measures based on data collected from deep space satellites, such as NOAA's Deep Space Climate Observatory, weather balloons, radar systems, and sometimes from IoT-based sensors. Today, AI is finding a role in weather forecasting with machine learning being employed to process more complex data in less time, with the hope of improving accuracy. For example, the Numerical Weather Prediction (NWP) site from NOAA offers a range of data sets for use by researchers, from temperature and precipitation data to wave heights, according to a recent account in Analytics Insight. The site offers vast data sets relayed from weather satellites, relay stations, and radiosondes to help deliver short-term weather forecasts or long-term climate predictions. Besides machine learning, other AI techniques for weather predictions include Artificial Neural Networks, Ensemble Neural Networks, Backpropagation Networks, Radial Basis Function Networks, General Regression Neural Networks, Genetic Algorithms, Multilayer Perceptrons and fuzzy clustering.