A surge of Arctic air brought strong winds, heavy snow and frigid temperatures to the Great Lakes and Northeast on Tuesday, following a bomb cyclone that swept across the Midwest. The storm left tens of thousands without power and created treacherous travel conditions.

The National Weather Service reported that blustery winds exacerbated the chill, with low temperatures expected to dip below freezing as far south as the Florida panhandle. The wild storm impacted parts of the Plains and Great Lakes this week, characterized by sharply colder air, strong winds, and a mix of snow, ice, and rain. Forecasters classified the system as a bomb cyclone due to its rapid intensification, indicated by a significant drop in pressure.

Kristen Schultz, traveling home to Alaska, recounted her four-hour journey to the Minneapolis airport on Tuesday. She advised travelers to "give yourself plenty of extra time and that way, even if things go smoothly, you dont have to be stressed out, and youre ready in case things dont go so smoothly."

Poweroutage.us reported that more than 115,000 customers nationwide were without power Tuesday morning, with approximately one-third of those outages occurring in Michigan.

The storm's rapid intensification into a bomb cyclone is a phenomenon that can be better understood and predicted through advancements in weather forecasting models. These models increasingly rely on artificial intelligence (AI) and machine learning (ML) to process vast amounts of atmospheric data, including temperature, pressure, wind speed, and humidity. AI algorithms can identify patterns and predict storm behavior with greater accuracy than traditional methods.

The use of AI in weather forecasting has significant implications for society. Improved predictions can lead to better preparedness for extreme weather events, potentially reducing property damage and saving lives. For example, AI-powered systems can provide more accurate warnings about the intensity and trajectory of storms, allowing communities to evacuate in a timely manner and take other necessary precautions.

Recent developments in AI-driven weather forecasting include the use of deep learning models that can learn complex relationships between different atmospheric variables. These models are trained on historical weather data and can be used to predict future weather patterns with increasing accuracy. However, challenges remain in developing AI models that can accurately predict extreme weather events, which are often influenced by a complex interplay of factors.

As the storm system moves into Canada, forecasters continue to monitor its progress and provide updated predictions. The integration of AI into weather forecasting is expected to play an increasingly important role in mitigating the impacts of future extreme weather events.