Hurricane Ida hit Louisiana in 2020 with 150 mph winds before moving up the East Coast to drop a record amount of rain on New York City. In its wake, the Category 4 storm caused severe flooding and destroyed just about everything in its path, with the cost to insurers estimated by leading risk management analytics firm RMS to be between $31 billion and $44 billion.
Hurricanes don’t just destroy properties though – they also destroy infrastructure and disrupt freight transport, especially by truck and rail. The damage from weather events is pervasive, but advanced weather data analytics that combine legacy and new datasets can improve the understanding of storm threats. With more data to inform analysis, those affected can better prepare to prevent losses and destruction that cause prices to rise, particularly those related to ground transport.
Immediately after Ida, digital brokerage platform Transfix reported that dry-van spot rates, or the cost of space in an enclosed trailer, rose 5 to 8 percent out of Memphis, about 5 percent out of Georgia, and up to 15 percent out of Louisiana and Mississippi. Freight rail operators suffered gridlock from the storm, as well as crews that had to wait for floodwater to recede before starting repairs. Freight cars were then rerouted or ran under limited service until the railroad network was fully operational.
Any slowdown in transport or increase in rates likely results in an increase in the prices consumers pay for those goods being transported. While some of these increases are inevitable, given enough information and forewarning, freight carriers can plan accordingly for these events, especially since few areas within the U.S. aren’t affected by Mother Nature.
Weather Events Disrupt Supply Chains
High winds and tornados disrupt trucking as they can push a high-profile vehicle out of its lane or in the worst case, flip it over. Flashfloods can wash away railroad tracks and potentially cause derailments. Rain and fog lead to decreased visibility for locomotive engineers and truck drivers, cutting speed by as much as 25 percent. Snow and ice can make roads impassable and tracks brittle. A wildfire destroys everything in its path, including roads and rails.
Infrastructure damage frequently causes disruption to, and in the worst-case halts, supply chains. While these slowdowns certainly can create compounding issues downstream in the manufacturing, wholesale, and retail sectors, transportation businesses are typically the first to suffer financial tolls ranging from penalties for missed deliveries to loss of equipment and freight damages.
The estimated annual cost of weather-related delays to trucking companies range from $2.2 billion to $3.5 billion, according to the U.S. Department of Transportation. There are about 700 railroads operating in the U.S., and North America’s largest freight rail operator, Union Pacific, suffered about $100 million in losses from wildfires and heavy rains that caused disruption over the company’s 32,000-mile network in 2021.
Extreme weather events are becoming more prevalent as weather patterns are changing, and they’re becoming more costly to all. Since 1980, the U.S. has experienced over 300 weather and climate disasters that have had overall damages exceeding $1 billion, for a total cost exceeding $2 trillion, according to the National Centers for Environmental Information (NCEI). The numbers have trended upward over time, with an average of about 16 events each year from 2016 to 2020 and 18 events topping $1 billion occurring in 2021 just through October 8. This number doesn’t include the tornados that tore through Mississippi in December or the Colorado wildfires that closed out the year.
Developing advanced planning and forecasting tools to aid in deciding whether cargo should go or not requires data that’s more timely, frequent, and accurate than what’s readily available today. However, recent advances in satellite-based weather observation systems and big data processing enable substantial progress towards a better understanding of storms.
More Data Makes a Difference
While technology can’t change weather patterns, it can be used to create innovative solutions for determining where a storm is, how severe it is, and where it’s going. The current weather forecasting systems gather an insufficient amount of actionable data that can be analyzed in real time. If only a few datapoints are available, for example, forecasting models must interpolate data to fill in the gaps. Given more real-time observations, the forecast accuracy can be vastly improved.
Less densely populated geographic regions typically have the fewest weather data collections – yet these open spaces are where many of the country’s major highways and rail corridors run. Gathering data across these zones utilizing satellite-based observation platforms can augment limited ground-based radars. Data from multiple sources provides more accurate predictions of weather events before they transpire, as well as their severity and movement as they’re ongoing.
There are a variety of methods used to gather weather data, including ground-based radar, sensors, and instruments; weather balloons; and aircraft-mounted radar and sensing equipment. These technologies don’t provide the same insight as some satellite sensors and cameras, though. Clear Weather visual systems aboard satellites capture a view of the ground during clear skies, and the tops of clouds during storms. All Weather technologies, such as passive microwave sensing, are capable of penetrating through clouds to gather information inside the storms to collect critical data, such as water volume, temperature, and precipitation type.
Weather information from any source is useful when it comes to understanding weather fronts, but the right detail helps create a more complete picture. Microwave-sensing technologies give unique insight into weather patterns that provide clarity as to the severity of a weather event. Despite its value, there are only 11 operating microwave sensors in orbit today, making the ability to see and predict fast-moving storms very limited when the satellite is only able to provide data for a region in the U.S. every three to six hours.
Align and Analyze for Better Forecasts
Quickly putting all the pieces together to gain better understanding of what’s happening now and forecasting what will happen as conditions change is no simple process. This process requires a significant amount of computing power to align and combine disparate datasets for a multidimensional representation of weather at a specific location and time. With a high-definition cohesive picture of the weather, traditional forecasts can truly become nowcasts.
The broadest view of the weather with the most forward-looking data comes from satellites though, and until recently, the few in orbit were flown by governments with multi-billion-dollar price tags.
Now, commercial satellite providers are putting more sensing equipment into orbit, and each new satellite sensor in orbit collects data that adds to forecast certainty and enables preemptive actions to reduce losses and impacts.
Being able to move goods predictably and safely is a key component of supply chains. Transportation companies need to be able to properly assess how infrastructure is affected by weather and the resources needed to make repairs or delay transport. Accurately planning for these events can prevent losses. Just getting trucks off roads and planning rail network actions before a storm could save big dollars, and having the right weather data drives the right decisions.
