Hurricane intensity is historically a very difficult thing to predict accurately. One example is Hurricane Patricia in the Northeast Pacific Ocean, which moved from a Category 1 storm into a Catagory 5 within the timeframe of just 24 hours. In order to have more reliable predictions for hurricane intensity, researchers led by scientists at NASA’s Jet Propulsion Laboratory in Southern California are tapping machine learning and artificial intelligence to improve the accuracy of their forecast—especially when it comes to "rapid-intensification events."
Still in its testing stages, described the AI model in a paper, published on August 25th in the Geophysical Research Letters journal. "It’s an important forecast to get right because of the potential for harm to people and property," said Hui Su, an atmospheric scientist at JPL.
Hurricane Intensity-Predicting AI
NASA Uses Machine Learning to Improve Its Whether Forecasts
Trend Themes
1. Hurricane Intensity-predicting AI - The use of machine learning and artificial intelligence to improve the accuracy of hurricane intensity forecasts offers disruptive innovation opportunities in the field of weather forecasting.
2. Rapid-intensification Event Prediction - The development of AI models specifically targeting rapid-intensification events during hurricanes could lead to disruptive innovation opportunities in disaster preparedness and emergency response industries.
3. Remote Sensing and Weather Forecasting - Incorporating remote sensing techniques such as satellite imagery with machine learning algorithms could provide disruptive innovation opportunities in the field of weather forecasting and natural disaster management.
Industry Implications
1. Weather Forecasting - The application of machine learning and artificial intelligence in weather forecasting has the potential to disrupt the industry and improve the accuracy of weather predictions.
2. Emergency Response - The accurate prediction of hurricane intensity, especially during rapid-intensification events, can help emergency response industries prepare and respond more quickly and effectively in the event of a natural disaster.
3. Satellite Imagery and Remote Sensing - The integration of machine learning and remote sensing techniques can revolutionize the field by providing a more complete picture of weather patterns and natural disasters to researchers and forecasters.