Collaborating Authors

IBM is turning to your smartphone to improve weather forecasts


IBM and its subsidiary The Weather Company are working on a new weather forecasting system, one that they say will boost forecast accuracy quite a bit. It's called the Global High-Resolution Atmospheric Forecasting System, or GRAF, and it will pull data from weather stations, aircraft sensors and smartphone pressure sensors -- a massive amount of information that will be analyzed by the IBM technology that powers the US Department of Energy's powerful Summit and Sierra supercomputers. "Today, weather forecasts around the world are not created equal, so we are changing that," Cameron Clayton, general manager of Watson Media and Weather for IBM, said in a statement. "Weather influences what people do day-to-day and is arguably the most important external swing factor in business performance. As extreme weather becomes more common, our new weather system will ensure every person and organization around the world has access to more accurate, more finely-tuned weather forecasts."

IBM Wants to Use Your Data to Create Hyper-Accurate Weather Forecasts

TIME - Tech

IBM on Tuesday unveiled a global weather modeling system that will combine data from smartphones and aircraft to produce what it says will be hyper-accurate local forecasts. The system, called the IBM Global High-Resolution Atmospheric Forecasting System, or GRAF, will create a one-day forecast updating every hour at a resolution of 3 kilometers, or about 1.9 miles -- a notable upgrade for many parts of the world. The company is pitching GRAF as particularly useful in industries that depend on accurate short-term weather forecasting, like agriculture and transportation, and especially in developing nations with less sophisticated meteorological infrastructure. "This is the first introduction of crowdsourced data, and to me, it's really opening a new era equivalent to what happened when we got satellite data in the 1980s," says Mary Glackin, VP of Weather Business Solutions at IBM. "Cell phone pressures are the start of this, but one could imagine data coming off of vehicles, smart buildings, even wearables doing into the future." GRAF forecasts will be created in part with location and atmospheric pressure data collected from smartphones running The Weather Channel app.

Advances in weather prediction


Advances in weather forecasting are helping to improve environmental forecast, for example, of wildfire activity. Weather forecasting provides numerous societal benefits, from extreme weather warnings to agricultural planning. In recent decades, advances in forecasting have been rapid, arising from improved observations and models, and better integration of these through data assimilation and related techniques. Further improvements are not yet constrained by limits on predictability. Better forecasting, in turn, can contribute to a wide range of environmental forecasting, from forest-fire smoke to bird migrations.

How Companies Like IBM Are Helping Predict Weather Better With AI


Each year, natural catastrophes result in severe economic costs and impact the lives of millions globally. While the forces of nature cannot be controlled, accurate and reliable weather forecasts will enable governments and administrations to take appropriate measures and contain the magnitude of the resultant damage. Likewise, precise weather predictions would benefit businesses by allowing them to take more appropriate decisions by factoring in weather related impacts. Over the years, weather predictions have greatly improved, however, there is still room for growth. Here's a look at how advanced technologies such as Artificial Intelligence (AI) are being leveraged to transform weather forecasting.

Deep Learning Accurately Forecasts Heat Waves, Cold Spells


Rice University engineers have created a deep learning computer system that taught itself to accurately predict extreme weather events, like heat waves, up to five days in advance using minimal information about current weather conditions. Ironically, Rice's self-learning "capsule neural network" uses an analog method of weather forecasting that computers made obsolete in the 1950s. During training, it examines hundreds of pairs of maps. Each map shows surface temperatures and air pressures at five-kilometers height, and each pair shows those conditions several days apart. The training includes scenarios that produced extreme weather -- extended hot and cold spells that can lead to deadly heat waves and winter storms.