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By NASA
Honolulu is pictured here beside a calm sea in 2017. A JPL technology recently detected and confirmed a tsunami up to 45 minutes prior to detection by tide gauges in Hawaii, and it estimated the speed of the wave to be over 580 miles per hour (260 meters per second) near the coast.NASA/JPL-Caltech A massive earthquake and subsequent tsunami off Russia in late July tested an experimental detection system that had deployed a critical component just the day before.
A recent tsunami triggered by a magnitude 8.8 earthquake off Russia’s Kamchatka Peninsula sent pressure waves to the upper layer of the atmosphere, NASA scientists have reported. While the tsunami did not wreak widespread damage, it was an early test for a detection system being developed at the agency’s Jet Propulsion Laboratory in Southern California.
Called GUARDIAN (GNSS Upper Atmospheric Real-time Disaster Information and Alert Network), the experimental technology “functioned to its full extent,” said Camille Martire, one of its developers at JPL. The system flagged distortions in the atmosphere and issued notifications to subscribed subject matter experts in as little as 20 minutes after the quake. It confirmed signs of the approaching tsunami about 30 to 40 minutes before waves made landfall in Hawaii and sites across the Pacific on July 29 (local time).
“Those extra minutes of knowing something is coming could make a real difference when it comes to warning communities in the path,” said JPL scientist Siddharth Krishnamoorthy.
Near-real-time outputs from GUARDIAN must be interpreted by experts trained to identify the signs of tsunamis. But already it’s one of the fastest monitoring tools of its kind: Within about 10 minutes of receiving data, it can produce a snapshot of a tsunami’s rumble reaching the upper atmosphere.
The dots in this graph indicate wave disturbances in the ionosphere as measured be-tween ground stations and navigation satellites. The initial spike shows the acoustic wave coming from the epicenter of the July 29 quake that caused the tsunami; the red squiggle shows the gravity wave the tsunami generated.NASA/JPL-Caltech The goal of GUARDIAN is to augment existing early warning systems. A key question after a major undersea earthquake is whether a tsunami was generated. Today, forecasters use seismic data as a proxy to predict if and where a tsunami could occur, and they rely on sea-based instruments to confirm that a tsunami is passing by. Deep-ocean pressure sensors remain the gold standard when it comes to sizing up waves, but they are expensive and sparse in locations.
“NASA’s GUARDIAN can help fill the gaps,” said Christopher Moore, director of the National Oceanic and Atmospheric Administration Center for Tsunami Research. “It provides one more piece of information, one more valuable data point, that can help us determine, yes, we need to make the call to evacuate.”
Moore noted that GUARDIAN adds a unique perspective: It’s able to sense sea surface motion from high above Earth, globally and in near-real-time.
Bill Fry, chair of the United Nations technical working group responsible for tsunami early warning in the Pacific, said GUARDIAN is part of a technological “paradigm shift.” By directly observing ocean dynamics from space, “GUARDIAN is absolutely something that we in the early warning community are looking for to help underpin next generation forecasting.”
How GUARDIAN works
GUARDIAN takes advantage of tsunami physics. During a tsunami, many square miles of the ocean surface can rise and fall nearly in unison. This displaces a significant amount of air above it, sending low-frequency sound and gravity waves speeding upwards toward space. The waves interact with the charged particles of the upper atmosphere — the ionosphere — where they slightly distort the radio signals coming down to scientific ground stations of GPS and other positioning and timing satellites. These satellites are known collectively as the Global Navigation Satellite System (GNSS).
While GNSS processing methods on Earth correct for such distortions, GUARDIAN uses them as clues.
SWOT Satellite Measures Pacific Tsunami The software scours a trove of data transmitted to more than 350 continuously operating GNSS ground stations around the world. It can potentially identify evidence of a tsunami up to about 745 miles (1,200 kilometers) from a given station. In ideal situations, vulnerable coastal communities near a GNSS station could know when a tsunami was heading their way and authorities would have as much as 1 hour and 20 minutes to evacuate the low-lying areas, thereby saving countless lives and property.
Key to this effort is the network of GNSS stations around the world supported by NASA’s Space Geodesy Project and Global GNSS Network, as well as JPL’s Global Differential GPS network that transmits the data in real time.
The Kamchatka event offered a timely case study for GUARDIAN. A day before the quake off Russia’s northeast coast, the team had deployed two new elements that were years in the making: an artificial intelligence to mine signals of interest and an accompanying prototype messaging system.
Both were put to the test when one of the strongest earthquakes ever recorded spawned a tsunami traveling hundreds of miles per hour across the Pacific Ocean. Having been trained to spot the kinds of atmospheric distortions caused by a tsunami, GUARDIAN flagged the signals for human review and notified subscribed subject matter experts.
Notably, tsunamis are most often caused by large undersea earthquakes, but not always. Volcanic eruptions, underwater landslides, and certain weather conditions in some geographic locations can all produce dangerous waves. An advantage of GUARDIAN is that it doesn’t require information on what caused a tsunami; rather, it can detect that one was generated and then can alert the authorities to help minimize the loss of life and property.
While there’s no silver bullet to stop a tsunami from making landfall, “GUARDIAN has real potential to help by providing open access to this data,” said Adrienne Moseley, co-director of the Joint Australian Tsunami Warning Centre. “Tsunamis don’t respect national boundaries. We need to be able to share data around the whole region to be able to make assessments about the threat for all exposed coastlines.”
To learn more about GUARDIAN, visit:
https://guardian.jpl.nasa.gov
News Media Contacts
Jane J. Lee / Andrew Wang
Jet Propulsion Laboratory, Pasadena, Calif.
626-379-6874 / 818-354-0307
jane.j.lee@jpl.nasa.gov / andrew.wang@jpl.nasa.gov
Written by Sally Younger
2025-117
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While auroras are a beautiful sight on Earth, the solar activity that causes them can wreak havoc with space-based infrastructure like satellites. Using artificial intelligence to predict these disruptive solar events was a focus of KX’s work with FDL.Credit: Sebastian Saarloos In the summer of 2024, people across North America were amazed when auroras lit up the night sky across their hometowns, but the same solar activity that makes auroras can cause disruptions to satellites that are essential to systems on Earth. The solution to predicting these solar events and warning satellite operators may come through artificial intelligence.
The Frontier Development Lab of Mountain View, California, is an ongoing partnership between NASA and commercial AI firms to apply advanced machine learning to problems that matter to the agency and beyond. Since 2016, the Frontier Development Lab has applied AI on behalf of NASA in planetary defense, Heliophysics, Earth science, medicine, and lunar exploration.
Through a collaboration with a company called KX Systems, the Frontier Development Lab looked to use proven software in an innovative new way. The company’s flagship data analytics software, called kdb+, is typically used in the financial industry to keep track of rapid shifts in market trends, but the company was exploring how it could be used in space.
Between 2017 and 2019, KX Systems participated in the Frontier Development Lab partnership through NASA’s Ames Research Center in Silicon Valley, California. Working with NASA scientists, KX applied the capabilities of kdb+ to searching for exoplanets and predicting space weather, areas which could be improved with AI models. One question the Frontier Development Lab worked to answer was whether kdb+ could forecast the kind of space weather that creates the auroras to predict when GPS satellites might experience signal interruption due to the Sun.
By importing several datasets monitoring the ionosphere, solar activity, and Earth’s magnetic field, then applying machine learning algorithms to them, the Frontier Development Lab researchers were able to predict disruptive events up to 24 hours in advance.
While this was a scientific application of AI, KX Systems says some of this development work has made it back into its commercial offerings, as there are similarities between AI models developed to find patterns in satellite signal losses and ones that predict maintenance needs for industrial manufacturing equipment.
A division of FD Technologies plc., KX Systems is a technology company that offers database management and analytics software for customers that need to make decisions quickly. While KX started in 1993, its AI-driven business has grown considerably, and the company credits work done with NASA for accelerating some of its capabilities.
From protecting valuable satellites to keeping manufacturing lines moving at top performance, pairing NASA’s expertise with commercial ingenuity is a combination for success.
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