Amanda Maxwell

Jun 12th 2020

Tsunamis, Hurricanes, Eruptions: Predicting a Natural Disaster


Big Brother may be watching you, but he has your best interest at heart. In the evolving science of natural disaster prediction, collecting lots of data makes the impossible a little easier.

Predicting Natural Disasters

Meteorology for natural events isn’t an exact science. Despite advances in technology, no one can tell with complete accuracy when a volcano will erupt, or how powerful a hurricane will be on landfall.

However, observation and data are powerful tools, so prediction is getting better and faster.

Throughout history, people have known about the potential for natural disasters. Storytelling cultures handed down the “data” from previous events to keep communities aware. For example, Pacific Northwest oral traditions tell of a great earthquake and resulting tsunami to warn people about what to do in future events. Recently, Simeuluean islanders escaped harm during the tsunami following the 2004 Indian Ocean earthquake because of similar stories handed down through the generations.

Today, instrumentation and digital data supplement visual observation and oral histories. Analysts use many sources to gather data.

Earthquakes and Tsunamis

Seismic instruments measure shaking in the Earth’s crust as geological fault lines slip. In the deep ocean, sensors monitor volume displacements and seabed deformation. Increasing activity can predict an earthquake.

Along one of the world’s most famous fault lines, the San Andreas Fault in California, the U.S. Geological Survey (USGS) collects data from tilt meters and creep meters that precisely measure earth movement; however, strain meters and pressure sensors embedded into the rock warn of pressure building up before a slip.

The National Oceanic and Atmospheric Administration (NOAA) collects data from a chain of early-warning oceanic devices including wave-height sensors and Deep-ocean Assessment and Reporting of Tsunamis (DART) buoys. These seabed sensors monitor seismic events beneath the waves then transmit the alerts back to land via surface buoys for coastal tsunami alerts.

Storm Watching

Meteorology recognizes patterns in weather data collected through remote sensing and on-the-ground observation that show hurricanes or tornadoes developing. Satellites give weather scientists a “big picture” overview of weather development, which is useful in storm tracking.

Tornadoes, however, are trickier to predict since they are much faster-moving events; meteorologists use Doppler radar to measure moving objects such as hail or rain within developing supercell clouds. Improvements in radar have increased advanced warnings from around three minutes in the late 1980s to 14 minutes’ notice in 2012.

Evolution and Future

Instrument upgrades and new technology are helping to improve natural-disaster prediction.

  • NOAA is deploying new, robust and easily launched DARTs for more widespread tsunami monitoring.
  • Meteorologists explore phased-array radar for storm watching; the multiple beams reduce scanning time for gathering data.
  • NASA geostationary and orbital satellites gather information on storms and other weather systems. They are also useful in conjunction with GPS and topographical scanning to show horizontal and vertical landmass shifts due to fault line activity.
  • Back on Earth, tornado scientists gather data within the storm itself by planting protected radar arrays in tornado country.
  • Volcanologists listen to volcanoes to predict eruptions. Infrasound waves at frequencies lower than what human ears can detect show when a volcano is “grumbling” from magma expansion and lava-cone collapse.
  • Satellite imagery and seismic data play a role in future landslide prediction; identifying and exploring events not usually detected in remote areas helps scientists predict risk in similar geographies.

Data and More Data

Prediction science is only as good as the data collected, but it is often difficult to gather it safely; for this reason, remote sensing is a valuable tool.

Automated vehicles can capture data right at the heart of a disaster. By dropping such aircraft into developing storms, scientists gain valuable information on hurricane formation, for example, that predicts behavior and damage on landfall.

RQ-4 on the ground with lighting striking in the background

Developed for remote eye-in-the-sky missions, the Northrop Grumman unmanned aerial reconnaissance vehicle RQ-4 Global Hawk is currently involved in storm-sensing missions. Flying the aircraft as part of a NASA project from the U.S. East Coast gave scientists more fly time over Atlantic storms. Since the Global Hawk operates over long ranges without refueling, it reaches remote weather systems faster to gather continuous data over longer periods than manned flights. This type of data collection is highly valuable.

Using different sensors to measure the temperature, pressure, relative humidity, amount of water vapor and liquid water, and wind speed and direction in a hurricane, unmanned vehicles such as the Global Hawk also collect valuable data after a disaster since they are often the only way to reach remote or isolated areas.

“This information is transmitted near-real time from the aircraft back to the Global Hawk Operations Center and then disseminated to the NOAA, National Hurricane Center (NHC) and the scientific community,” says Mick Jaggers, Northrop Grumman’s vice president and program manager for the Global Hawk program.

Both the Global Hawk and the MQ-4C Triton have engaged in disaster surveillance. Global Hawk took part in Operation Tomodachi to relay vital visuals on earthquake and tsunami damage to towns and the Fukushima nuclear plant in 2011. Information like this is also valuable for predicting future disasters; building damage recorded after the Haitian earthquake in 2010 could help efforts to rebuild to a safer code.

Cathedral in Haiti: Before and After the 2010 Earthquake - photo submitted

Jaggers offers further detail on the program’s success in acquiring crucial information: “During the 2016 campaign, the Global Hawk flew nine flights over hurricanes Gaston, Hermine, Matthew and Nicole and Tropical Storm Karl,” says Jaggers. “Based on Global Hawk data from then-Tropical Storm Gaston, the NHC upgraded the storm to hurricane status, the first time that data from an unmanned aircraft had been used to change the category of a storm.

“NOAA scientists determined that the Global Hawk data provided statistically significant improvements in their hurricane prediction models; in addition, Global Hawk data over the Atlantic Ocean were shown to improve weather predictions for the Pacific Ocean,” he adds. “The long endurance, long range and high altitude of the aircraft, coupled with the team’s flexibility, make the Global Hawk an important part of future airborne weather science missions.”

Researchers also seek better ways to analyze and present data for faster and more accurate predictions. Combined systems like EDSS (Environmental Decision Support System; Northrop Grumman) aggregate data sets, then present them in an easily accessible manner to answer key questions — where? when? why? what?

Improving analysis also boosts predictive power; USGS researchers are examining fractal mathematics for improved data analysis. Compared to traditional statistical methods, fractals gave more information on past hurricane events. For future hurricanes, in terms of predictive power, fractal-based predictions are much more precise.

Order out of chaos? Prediction out of whirlwinds? You could make a difference in natural disaster prediction — check out the Northrop Grumman careers page for details.