From 1938 to AI: How Emergency Management Is Evolving with Improved Forecasting 

As the great New England Hurricane of 1938 approached Long Island, NY, there were minimal warnings about the imminent storm that would claim the lives of over 600 people. The storm made landfall as a large Category 3 hurricane, with wind speeds reaching 187 mph in Massachusetts and storm surges exceeding 15 feet in Rhode Island. At that time, no satellites were tracking the storm's movement, and computers had not yet been developed to analyze complex differential equations for predicting the future state of the atmosphere. 

Weather prediction was a highly labor-intensive process that required considerable effort to gather observations from weather stations and ships for manual data analysis to determine storm locations. Meteorologists at the U.S. Weather Bureau would compare trends in storm movement over time and apply established rules of thumb and other knowledge to forecast its progression. However, because this synthesis of information was time-consuming, its accuracy would quickly diminish.  

Meteorologists from 1938 would be amazed by today’s advanced weather forecasting, which now includes sophisticated dynamical models, remote sensing technology, and improved communication systems. Now, we’re entering a new era: AI-driven models are improving forecast accuracy and reshaping how we respond.  

Innovative platforms, such as PRATUS, are at the forefront of this shift. By combining AI-powered predictions with real-time decision-support tools, PRATUS helps communities plan more effectively, respond faster, and recover swiftly from extreme weather events.

This blog post covers:

• Storm Track Relationship to Wind and Water Hazards
• Evacuation Decisions
• The Forecast Cone
• AI and Forecast Track Improvements
• Modernize Emergency Management with PRATUS™

Storm Track Relationship to Wind and Water Hazards 

Hurricanes and tropical storms, hereafter tropical cyclones, cause damage primarily through two types of hazards: wind and water. The severity of wind and water hazards is highly influenced by the storm's path; just a few miles can mean the difference between limited and severe impacts. Predicting potential damage relies heavily on accurate forecasts of both the storm's track and intensity. Additionally, various factors, such as the storm's size and its interactions with local environments, can further influence localized effects. 

Wind damage primarily results from extensive, sustained wind fields, which can significantly impact power grid infrastructure. On average, a North Atlantic hurricane generates a hurricane-force wind field (sustained winds of 74 mph or greater) that extends approximately 50 miles from the storm center. However, this distance can vary widely, from as small as 20 miles (as seen in Hurricane Camille in 1969) to over 100 miles (as seen in Hurricane Ike in 2008). 

Figure 1 illustrates the peak size of hurricane wind fields for North Atlantic hurricanes, demonstrating a wide range of storm sizes. Storms that undergo re-intensification and transition into extratropical hybrids, such as Hurricane Sandy in 2012 and Hurricane Helene in 2024, can produce huge wind fields that maintain high intensity well inland due to interactions with weather fronts and the jet stream. On a smaller scale, tornadoes that form within the spiral rain bands of these storms can also cause significant damage, with Hurricane Milton in 2024 serving as an exceptional example. 

The gradient of wind intensity and storm surge impacts can vary significantly from one county to another. For instance, during Hurricane Helene (2024), peak winds ranged from 80 mph to 50 mph across a distance of less than 50 miles (see Figure 2). Generally, the highest winds and storm surge associated with landfalling storms occur to the right of the storm track, specifically to the east of northward-moving storms. 

Water hazards lead to significant losses of life and property. These hazards can be categorized into two main types: storm surge along coastlines and inland flooding caused by heavy rainfall. 

Storm surge occurs when sea levels rise due to wind and wave action. Additionally, lower atmospheric pressure contributes to this rise, while onshore winds and wave activity further increase water levels. The shape of the coastline and the characteristics of the terrain, both above and below the water, are crucial factors that influence the local depth of storm surge.  

Evacuation zones are primarily determined by elevation above sea level, given the severe consequences associated with storm surge. 

Inland flooding hazards vary based on local hydrology risk factors. Heavy rainfall within rainbands, as well as interactions with midlatitude systems (such as fronts and the jet stream) and terrain, can lead to catastrophic flooding, even if the storm only reaches tropical storm intensity (as seen with Ida in 2021). Slow-moving storms, like Harvey in 2017, pose an especially high risk. Additionally, faster-moving storms along the U.S. East Coast have caused significant flooding (examples include Floyd in 1999 and Irene in 2011) as they interact with fronts and the jet stream, particularly to the left of the storm track. 

Figure 1. Peak size of the hurricane force winds around time of peak storm intensity by lowest pressure. For hurricane strength tropical cyclones from 1970-2023. Wind intensity is assumed to be symmetric about the storm center for visualization simplification. 

Figure 2. Hurricane Helene centerline storm track and observed peak 10-meter elevation 3-second wind gust. The highest winds occurred to the right of the track. 

Small changes in the path of storms, especially near densely populated areas, can pose significant challenges, particularly when storms move more parallel to the coastline (as seen with Ian in 2022 and Beryl in 2024). For example, 48 hours before landfall, the official forecast track errors from the National Hurricane Center were 50 miles for Beryl and 68 miles for Ian.  

While these forecasts were reasonably accurate and close to long-term performance standards, the forecast trends and the associated consequences of the storms moving parallel to the coast made them difficult to interpret. Hurricane Beryl followed a worst-case track, putting the greater Houston metro area in the zone of peak winds, while Ian made landfall as a Category 4 storm in southwest Florida. Even small improvements in track prediction can lead to significant enhancements in preparedness decisions. 

Evacuation Decisions 

Storm surge risk inundation areas and the associated evacuation zones determine the key decision points for evacuation during emergencies. Effective emergency evacuation planning is ideally conducted with enough lead time to carry out operations, while also ensuring precision to avoid unnecessary evacuations. More accurate forecasts reduce false alarms, save money, and protect lives. It is common for individuals who evacuated only to find limited damage upon their return to feel that their evacuation was unnecessary. However, the forecast error margins at the go/no-go decision point can be significant enough to justify an evacuation. Such experiences can lead to distrust in the system and create a false sense of security. 

As of 72 hours, or three days before the landfall of a tropical cyclone, the official forecast error from the National Hurricane Center for the 2024 season was 67 miles, comparable to the distance between the two counties. Given this error margin, it is reasonable to consider evacuating a few counties away from the predicted landfall location. 

It's important for individuals involved in emergency preparedness to follow evacuation orders when making decisions about evacuating. Each storm is unique, and relying too heavily on past experiences can lead to dangerous decision-making biases. Hurricanes and tropical storms are relatively rare occurrences in any given location, and perception bias often hinders an accurate understanding of the associated risks. 

The Forecast Cone 

The forecast cone provides a probabilistic representation of where a tropical cyclone is likely to track, based on historical forecast accuracy. This cone is often referred to as the "cone of uncertainty." It offers valuable insights into the potential path of the storm and is derived from a 5-year forecast error, calculated within one standard deviation of the average track error for forecasts issued by the National Hurricane Center.  

The forecast cone captures approximately two-thirds of the variability, meaning that about 67% of the time, the actual storm track falls within the predicted area of the cone. However, some storms exhibit higher or lower predictability due to various factors. For example, Figure 3 illustrates the size and shape of a northward-moving tropical cyclone near Florida. The cone widens as forecast variability increases over time and space. 

Predictability decreases, and forecast error increases, at longer forecast lead times due to the compounding sensitivity inherent in chaotic systems. This sensitivity can arise from initial conditions or from processes that models cannot fully resolve or capture within the dynamics of tropical cyclones. For example, this includes the feedback generated by the heat energy released within a tropical cyclone, which interacts with the larger wind circulation environment surrounding the storm. 

Figure 3. Forecast cone size depiction at different forecast lead times using a hypothetical northward moving tropical cyclone of 8 mph based on the forecast cone sizes using the National Hurricane Center 2020-2024 forecast errors. 

Using the forecast cone can be confusing because its shape and size represent a range of probable outcomes. This information should ultimately guide deterministic decisions. Additional risk factors that can affect damaging hazards include the storm's size, movement, and intensity, which can lead to significant impacts even far from the centerline track.  

Forecast validation from the 2024 hurricane season for the five storms that made landfall in the U.S. shows that all five storms were located within the forecast cone 60 hours before landfall. The forecast cone is a valuable tool for understanding the potential storm path, and it is considered best practice to take into account different track scenarios within the cone when planning for a storm. 

Figure 4. US landfalling hurricanes from the 2024 storm season and forecast cones from the National Hurricane Center at 60 hours ahead of landfall overlaid with the observed tracks. Landfalling occurred on the following dates - Beryl (July 8), Debby (August 5), Francine (September 11), Helene (September 26), and Milton (October 9). 

AI and Forecast Track Improvements 

The AI revolution is accelerating innovation in weather prediction. Various technological advances are enabling this change, such as the availability of high-quality training datasets, access to high-performance computing, and open data analysis packages to deploy AI-based training models. Traditional dynamical numerical weather forecasting models can have chronic structural issues, and AI-based solutions can be used to correct these biases and uncover new relationships within complex systems, thereby improving predictions. Tropical cyclone track prediction is one area that shows great promise using deep learning. 

A recent paper published in Nature by Bodner et al. (2025) showed significant improvements in the forecast accuracy for tropical cyclones, specifically a reduction in track errors of about 20% for Atlantic tropical cyclones. This advancement was achieved using deep learning within their Aurora modeling system framework. Silurian AI is now commercially deploying this work in preparation for the 2025 hurricane season. 

Aurora is a foundation model with 1.3 billion parameters, trained over a million hours of global Earth system data, which includes reanalysis, forecasts, and simulations. It employs a multimodal architecture consisting of a 3D token-based encoder, a Swin Transformer processor, and a Perceiver-style decoder, all working on high-resolution representations of atmospheric, oceanic, and surface conditions.  

Initially, Aurora is pre-trained to recognize general geophysical dynamics, and it is then fine-tuned for specific tasks, such as tropical cyclone forecasting, using a smaller, focused dataset. This approach makes it both efficient and versatile.  

In tests involving 5-day forecasts of tropical cyclone tracks in the Atlantic and eastern Pacific, fine-tuned Aurora outperformed all seven major global operational forecast centers, reducing the average track error by approximately 20–25%. Figure 5 illustrates a hypothetical 20% improvement in track error, showing even greater enhancements at longer lead times when critical storm preparedness decisions are made. 

In June 2025, Disaster Tech and Silurian AI launched a transformative partnership to reshape hurricane preparedness and emergency management. Through this collaboration, Disaster Tech’s decision-support platform, PRATUS, integrates Silurian AI’s cutting-edge GFT model alongside official forecasts from the National Hurricane Center. This dual-source approach will empower emergency managers and public safety leaders with side-by-side predictive insights, enabling faster, more confident decisions across planning, response, and recovery operations. 

Figure 5. Forecast cone size depiction at different forecast lead times using a hypothetical northward moving tropical cyclone of 8 mph based on the forecast cone sizes showing what a 20% track error reduction from AI-enhanced approaches could produce. 

Moreover, once trained, AI models can deliver these forecasts in minutes using a single TPU/GPU, while traditional systems on supercomputers may take hours. However, outputs from dynamic modeling systems are still necessary for making further improvements. In other words, AI-based deep learning will not replace dynamical modeling approaches anytime soon; instead, it will optimize solutions and uncover new relationships that were previously undiscoverable through other methods.  

When used appropriately alongside traditional approaches, AI can accelerate innovation. Building user trust will take time, but AI-augmented methods are already being adopted across government and the private sector, making their acceptance and integration into the field increasingly inevitable. 

Modernize Emergency Management with PRATUS™

Ready to modernize your hurricane response? Discover how PRATUS combines AI-enhanced forecasting, geospatial analytics, and real-time collaboration to support every phase of storm planning and recovery.