Critical maritime events present substantial social, environmental, and economic risks, particularly as climate change increases the frequency and severity of hazardous weather along major maritime trade routes. This paper presents a weather-aware vessel rerouting framework that integrates real-time meteorological forecasts with large-scale historical vessel traffic patterns from the Kpler MarineTraffic Platform observed under diverse sea conditions, aiming to enhance navigational safety during extreme weather events while maintaining maritime operational efficiency. The core of the proposed approach is a modified A* search algorithm, where edge weights are dynamically assigned based on forecasted weather severity and historical vessel trip density, as a function of the prevailing sea state. The approach is evaluated using randomly selected origin-destination pairs across the southeastern U.S. Coast in September 2022, a period when a range of weather scenarios in terms of severity, spatial extent, and duration. Results indicate a 16.97% reduction in median cumulative weather penalties in variable sea conditions and a 35.03% decrease very extreme sea conditions when compared to shortest-path routing. A case study for Hurricane Fiona (Category 4, 21 September 2022) further demonstrates the system's ability to entirely avoid areas forecasted to experience extreme sea conditions. Findings highlight the value of integrating AIS-based collective fleet intelligence with weather data from historical databases to improve voyage planning and vessel operations in dynamically changing sea conditions.

Monitoring vessel traffic on a global scale is a complex and challenging task. The large number of moving vessels and the complexity of monitoring their position and forecasting their route in real-time require novel, advanced and highly scalable big-data mechanisms. In this work a digital twin for constant maritime situational awareness on a global scale is presented. The described multi-layered system is able to visualize maritime traffic in real-time, based on data from the Automatic Identification System (AIS), while also providing forecasts of future movement based on machine learning and deep learning techniques. The system is validated using real streaming AIS data from around the globe to demonstrate its performance, scalability and parallelization efficiency.