The Missing Piece: AI-Driven Weather Data Aggregation

We live in an era of unpredictable weather. As unpredictable weather incidents increase on a global scale, the demand for reliable weather prediction services is on the constant rise. Countless industries are fueling this demand – but perhaps none more dominantly than the aviation and maritime industries, whose operations are directly impacted by weather on practically every level. Business projections indicate a significant growth for the weather forecasting market from today to 2034, with a CAGR of 12% and a projected market valuation of approximately 5 billion dollars.

Rough weather gets all the headlines, yet today’s weather prediction solutions are actually very advanced. Today’s real-time weather predictions aggregate data from various sources including satellites, land-based weather stations and ocean sensor buoys. This data is analyzed via proven models such as the ECMWF’s Integrated Forecasting System (IFS) and the US National Weather Service’s Global Forecast System (GFS), to provide reliable large-scale weather forecasts. 

“These models are excellent at predicting major weather events. They’ll never miss a hurricane or massive storm”, says Dor Raviv, Orca AI’s Co-Founder and CTO. “Yet these systems are sometimes less effective when it comes to minor weather events such as fog, thunderstorms or squalls, which impact maritime vessels during specific voyage locations.” 

Rough seas ahead: The maritime industry weather challenge

Storms, high winds, rough seas and other extreme weather phenomena greatly increase the risk of collisions, capsizing, loss of life and cargo damage. In September of 2024, the World Meteorological Organization (WMO) and the International Maritime Organization (IMO) conducted a symposium to outline key actions that must be undertaken to better protect vessels at sea from weather-related dangers. 

Extreme weather can impact maritime vessels in many ways, from compromising safety to delaying ship docking. The following are the major possible implications:

Safety and Risk 

Aside from endangering crew safety, incidents caused by extreme weather often culminate in costly insurance claims and regulatory fines. Add higher insurance premiums, financial loss due to damaged vessels or cargo and mounting legal costs – and what starts out as a storm-related event can turn into a tornado of damages for fleet companies. 

Fuel Efficiency and Operational Costs

Maritime cost-cutting strategies often focus on fuel efficiency, yet when encountering bad weather, ships are often forced to execute practices that spend more fuel than expected – from sudden course alterations to speed increases and maneuvering on the fly. Every detour can add extra mileage to the voyage, which in turn requires more fuel. In addition, the resistance created by strong winds and turbulent waters can increase the ship’s energy usage – which translates into added costs. 

Delivery and Scheduling

Delivery and punctuality go hand-in-hand, and even the slightest delays cost cargo vessels vast amounts of money in terms of port manpower, insurance coverage and more. Unforeseen weather is a major source for vessel arrival and departure delays, which influences the entire supply chain. This creates volatile chain reactions that can influence customer satisfaction and tarnish the reputations of commercial fleets, which in turn can lead to order cancellations, more port fees, additional maintenance activities, and additional payments to professional crews.   

Asset Maintenance and Longevity

Vessel wear-and-tear is a natural and unavoidable occurrence, yet increased exposure to harsh weather increases the degradation of important ship components. From the ship’s hull to its most delicate machine constituents, bad weather induces added repair and maintenance sessions, while minimizing the vessel’s operational availability and reducing its overall lifespan. 

Environmental Compliance 

Today’s fleets must comply with strict environmental regulations regarding their vessels’ fuel efficiency and carbon emissions, which translates into constant efforts to optimize navigational routes, reduce fuel consumption and lower energy production. Unexpected adverse weather carries enough power to topple compliance strategies, forcing ships to alter their course and waste more fuel and energy than intended. Although unintentional, weather-induced noncompliance escalates carbon emissions, increases ocean pollution, and can lead to harsh penalties from governing bodies. 

The key to overcoming weather at sea: aggregated data

The rise of artificial intelligence has led to its successful penetration into the maritime industry as a powerful and essential tool aiding ships at sea. This reality presents an unprecedented opportunity for making real progress regarding the challenges posed by extreme weather.

The 2024 WMO-IMO symposium on extreme maritime weather was a follow-up to a similar symposium conducted in 2019 by the two entities. Although they were five years apart, both symposiums stressed two important factors for success: Collaboration and the interpretation of robust data. Both factors are currently being revolutionized by AI technology. 

AI powered systems deployed on seafaring vessels are designed to offer precise predictions based on vast data processed by cameras, sensors and other cutting-edge devices. These predictions are not only powered by immediate data, but also by machine learning capabilities trained on months and years of accumulated data from various vessels, which is aggregated and computed to provide a better understanding of maritime conditions – including unanticipated weather. 

Bridge lookout AI powered systems such as Orca AI’s SeaPod can detect and predict weather patterns with cutting-edge precision, even measuring unexpected ambient conditions such as poor visibility due to rain or fog, wave heights and more. SeaPod ascribes a specific weather or wind scale rating to every parameter it predicts or encounters, in real-time, giving crew members an easy understanding of the weather conditions the ship is about to face. This allows crews to avoid sudden maneuvering and accelerations that increase fuel consumption, while promoting the safety of the ship, its people and its cargo. 

View from Orca AI’s digital watchkeeper, SeaPod, at sea. Beaufort scale of 6-7.

Beaufort scale: 6-7, Wave height: 2.5M – 4M, Visibility conditions: Cloudy, Wind: 22-33 Knots

Better together: AI-driven ocean weather data aggregation

Data compiled from numerous sources, on a multitude of levels, allows AI systems to conduct effective ocean weather data aggregation. By processing factors that include wind patterns, wind strength, cloud characteristics, visibility conditions and wave heights, AI systems are able to provide ship controllers with an accurate picture of the weather up ahead, improving their preparedness in terms of safety, navigation and operational efficiency. 

In many ways, AI-based maritime weather data aggregation resembles the aggregation performed by the world’s vast weather forecasting systems, which combined data received from multiple sources. Yet AI weather aggregation is able to complement the crucial information delivered by these traditional systems with data that is much “closer to sea level” and can pinpoint many ambient characteristics that certain forecasting models often miss. These localized minor weather events – sometimes undetected by sophisticated satellite or land stations but effectively pinpointed by AI ship bridge lookouts – are often the absent piece maritime crews need in order to achieve their voyage objectives. 

Sharing weather estimations for effective fleet-level collaboration

Weather forecasting is always just part of the story; the second part is communicating precise weather data to relevant parties. Global weather and meteorological systems use a wide range of valuable applications to communicate their real time weather reports and predictions, including online and radio systems, which are of extreme value to ships at sea. 

Yet when a specific fleet uses an AI operated system across all of its vessels, an unexpected yet highly important advantage comes to light: the ability to communicate aggregate weather data from one vessel to all other fleet vessels, in real-time, as well as to all on-shore fleet managers. By sharing weather estimations across the entire fleet, one vessel is able to enhance the weather protectiveness of all other vessels on the same shipping lane, while also receiving relevant data that further boosts its weather awareness. Orca AI, for example, has developed a collaborative dashboard tool called FleetView, which connects all vessels via the real-time data collected and classified by their systems. FleetView allows fleet managers to view all data from all fleet vessels, notifying specific vessels regarding irregularities they should be aware of. 

Monitoring the weather better, with on-ship AI

Nothing can or should replace the vast national and continental systems that are monitoring weather and delivering forecasts that support our daily lives on land, in the air and at sea. Yet much like satellites, weather stations or buoys, AI is able to provide an additional layer of weather detection and monitoring. Unlike them, however, AI can predict future occurrences with groundbreaking accuracy while effectively enhancing its own data via a wide range of connective capabilities. In a sea of small, localized and isolated weather events, where a slightly denser fog or a wave a bit higher than expected can throw a cargo ship into the throes of the unknown, AI-based weather data aggregation often turns out to be the ultimate difference maker.