Artificial Intelligence and its Advantages in Weather Forecasting

By CIOReview | Wednesday, December 12, 2018

The prediction of an accurate weather forecasting can yield massive improvements for businesses and governments. Weather forecasting allows farmers to plan their planting and harvesting and take critical decisions for maximum output. Airline and shipping companies use weather forecasting for a more comfortable and safer ride. Businesses and governments use weather forecasting to prepare a response to any natural disaster. In 2018 there were eleven climate disasters in the United States in which each of the disasters accounted for a loss of almost 1 Billion dollars.

An incredible volume of weather-related data is produced every day through multiple channels. More than a thousand powerful weather satellites provide a massive volume of data about cloud patterns, winds, temperatures, and many other factors. Weather stations around the globe gather real-time weather data. IoT devices are also used for weather forecasting. Analysis of this kind of data can be very effective in future weather forecasting.

The US National Oceanic and Atmospheric Administration (NOAA) have been relying on artificial intelligence and machine learning techniques to improve their weather forecasts. Applying AI and ML techniques along with the physical understanding of the climate has significantly improved the prediction skill for high impact weather events like thunderstorms, tornados, and hurricanes.

Of all the extreme weather events, Thunderstorms account for 40 percent of them, and this causes damage of 14 percent of the entire loss and 17 percent of related deaths. The Geostationary Operational Environment Satellite (GOES) operated by NASA and NOAA uses AI techniques for all-sky infrared radiance.  This method shows the amount of radiation that is emitted by objects on earth at different infrared frequencies to predict extreme weather conditions. This method uses predictive analysis to anticipate any extreme weather conditions. For example, this process predicts supercell thunderstorms with atmospheric conditions with the help to data analytics.

Although this technique is in its early stages, it has proved to be very beneficial in predicting any extreme weather conditions.