As climate change intensifies extreme weather events, the need for faster and more precise weather predictions is crucial. Traditional forecasting methods, relying on complex physics-based models, are time-consuming and expensive. AI-driven tools offer a promising alternative, enabling quicker and potentially more accurate forecasts even on standard laptops. This is especially beneficial for developing nations with limited access to high-powered supercomputers, empowering them to better prepare for and mitigate the impacts of climate-related disasters.
ArticleBody:This story was originally published by Grist. Sign up for Grist’s weekly newsletter here. Every day, meteorologist Hannah Wangari takes the free graphs and maps produced by the five forecasting models she subscribes to and interprets what she sees. “What’s the likelihood of rain in different parts of the country?” she might wonder.
That is not unusual. Google’s GenCast, unveiled last month, outperformed traditional forecasting models on 97 percent of 1,320 metrics. Its predecessor, GraphCast, proved more accurate than the world’s premier conventional tool, run by the European Center for Medium-Range Weather Forecasts. “AI produces much better results than the physics-based models,” said Florian Pappenberger, deputy director general of the European Center, which plans to launch its own AI model this year.
AI Weather Forecasting Climate Change Disaster Preparedness Developing Nations
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