How Data Can Reduce the Impact of Hurricanes

Tropical storm Henri made landfall this week, the second tropical storm to hit the Northeast following Elsa. These storms align with NOAA’s prediction of an above-normal Atlantic hurricane season that is fueled by climate change and other factors. 

“ENSO-neutral and La Nina support the conditions associated with the ongoing high-activity era,” Matthew Rosencrans, the lead seasonal hurricane forecaster at NOAA’s Climate Prediction Center wrote. “Predicted warmer-than-average sea surface temperatures in the tropical Atlantic Ocean and the Caribbean Sea, weaker tropical Atlantic trade winds, and an enhanced west African monsoon will likely be factors in this year’s overall activity.” 

NOAA’s Climate Prediction Center is able to make these forecasts with a 70% confidence score. Insights this accurate are particularly tricky to obtain in extreme weather: expert meteorologists deploy the sturdiest buoys to predict hurricane impact. Studying storm surge is reactive work that requires flexibility. NOAA, the USGS, and other organizations monitor tropical storms with systems that can support multiple sensors and transmit data in real time.

Hurricane forecasting: how does it work?

There are two types of hurricane forecasting models. The first is the seasonal forecast: how much hurricane activity is expected to take place during hurricane season? The second is an individual storm forecast: what path is a hurricane expected to take? Data plays a major role in both types of hurricane forecasting models. 

Hurricane data for predicting seasonal storms

Hurricane season for the US is typically from June through November. Forecasting hurricanes begins in April every year. Meteorologists use data to anticipate how many “named storms” are predicted, as well as how many hurricanes will make landfall. Tropical storms are given a name when they reach the speed of 39 mph (and keep that name if they turn into hurricanes). In addition, scientists break down the number of predicted storms by their intensity (e.g., hurricane, tropical storm, intense hurricane, etc.). 

This type of hurricane forecasting also seeks to predict wind speeds and intensity of sustained winds using statistics. The data used comes from historic trends and climate factors, such as ENSO and sea surface temperature. Data is fed into statistical models to create fairly accurate probabilities. “Scientists cannot say that the third named storm of the season will hit Florida on June 30th.  They can only say that there is a five percent chance of a major hurricane hitting the coast from April to November,” explained one MIT paper.

Hurricane data for storm forecasting

The second type of hurricane forecasting covers specific storm events. Once a storm has formed, data can be used to track its probable trajectory and strength. Data is collected multiple times a day and fed into two international forecasting systems, the UK Meteorological Office’s global model and the US Navy Operational Global Atmospheric Predictions Systems. 

This data comes from a range of sources, including satellites and large, moored buoy platforms. Sofar Ocean operates the largest private network of drifting buoys in the world. Equipped with a Smart Mooring, a Spotter buoy is able to report wave height and spectrum, peak period, wind, current, and surface temperature measurements in real-time. 

[Read more: Improving Hurricane Observations with Data from Scalable Sensor Networks]  

How data reduces the impact of hurricanes

Sofar's modular systems prove capable across disciplines, able to optimize hurricane forecasts, improve flood maps, hasten hazard warnings, and hone sea-level rise predictions. A Spotter and Smart Mooring grants real-time access to information from anywhere in the ocean, no matter the conditions. The polyurethane Smart Mooring cable is tailored with a Kevlar braid to secure the Spotter in its place with an adjustable design that allows for integration of any sensor.

The experts at FEMA estimate that 90% of natural disasters in the US involve a flood. When Hurricane Sandy hit New York and New Jersey, flooding, high winds, and a 13+ foot storm surge caused $70 billion in damages. Since the storm, city officials and urban planners have started using storm data to create the infrastructure that would lessen the impact of another Sandy-sized storm. Berms, for instance, could provide a buffer zone to absorb flooding before it reaches critical levels. 

[Read more: Flood Maps Are Outdated - Here's How to Fix Them]  

Moored buoy systems such as those developed by Sofar Ocean are crucial to developing stronger early warning systems, providing coastal communities with enough time to evacuate, shelter in place, or receive aid after a storm has made landfall. The data used for seasonal hurricane forecasting can help families prepare well in advance. 

“Today you can determine your personal hurricane risk, find out if you live in a hurricane evacuation zone, and review/update insurance policies. You can also make a list of items to replenish hurricane emergency supplies and start thinking about how you will prepare your home for the coming hurricane season. If you live in hurricane-prone areas, you are encouraged to complete these simple preparations before hurricane season begins on June 1,” wrote NOAA.  


To learn more about hurricane tracking, visit the Sofar Ocean blog.

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