Residents in the Shiloh Community have fought for years to remedy excessive storm water runoff caused by an elevated highway. Now they’re suing.
Traditional weather forecasting is slow and expensive. AI could help

AI-driven weather forecasting models are proving faster, cheaper, and more accurate than traditional numerical methods reliant on expensive supercomputers. These tools leverage machine learning to analyze decades of open-source weather data, allowing meteorologists, like those in Kenya, to produce localized forecasts quickly. AI models, such as Google’s GenCast and Oxford’s rainfall prediction tool, offer significant improvements in accuracy and speed, aiding disaster preparedness and resource allocation. However, challenges remain, including limited ability to predict rare events and the environmental impact of energy-intensive data centers. Hybrid approaches combining AI with physics-based methods may further enhance capabilities.
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