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    City digital twins help train deep learning models to separate building facades

    Game engines were originally developed to build imaginary worlds for entertainment. However, these same engines can be used to build copies of real environments, that is, digital twins. Researchers from Osaka University have found a way to use the images that were automatically generated by digital city twins to train deep learning models that can efficiently analyze images of real cities and accurately separate the buildings that appear in them.
    A convolutional neural network is a deep learning neural network designed for processing structured arrays of data such as images. Such advancements in deep learning have fundamentally changed the way tasks, like architectural segmentation, are performed. However, an accurate deep convolutional neural network (DCNN) model needs a large volume of labeled training data and labeling these data can be a slow and extremely expensive manual undertaking.
    To create the synthetic digital city twin data, the investigators used a 3D city model from the PLATEAU platform, which contains 3D models of most Japanese cities at an extremely high level of detail. They loaded this model into the Unity game engine and created a camera setup on a virtual car, which drove around the city and acquired the virtual data images under various lighting and weather conditions. The Google Maps API was then used to obtain real street-level images of the same study area for the experiments.
    The researchers found that the digital city twin data leads to better results than purely virtual data with no real-world counterpart. Furthermore, adding synthetic data to a real dataset improves segmentation accuracy. However, most importantly, the investigators found that when a certain fraction of real data is included in the digital city twin synthetic dataset, the segmentation accuracy of the DCNN is boosted significantly. In fact, its performance becomes competitive with that of a DCNN trained on 100% real data. “These results reveal that our proposed synthetic dataset could potentially replace all the real images in the training set,” says Tomohiro Fukuda, the corresponding author of the paper.
    Automatically separating out the individual building facades that appear in an image is useful for construction management and architecture design, large-scale measurements for retrofits and energy analysis, and even visualizing building facades that have been demolished. The system was tested on multiple cities, demonstrating the proposed framework’s transferability. The hybrid dataset of real and synthetic data yields promising prediction results for most modern architectural styles. This makes it a promising approach for training DCNNs for architectural segmentation tasks in the future — without the need for costly manual data annotation.
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    Scientists see spins in a 2D magnet

    All magnets — from the simple souvenirs hanging on your refrigerator to the discs that give your computer memory to the powerful versions used in research labs — contain spinning quasiparticles called magnons. The direction one magnon spins can influence that of its neighbor, which affects the spin of its neighbor, and so on, yielding what are known as spin waves. Information can potentially be transmitted via spin waves more efficiently than with electricity, and magnons can serve as “quantum interconnects” that “glue” quantum bits together into powerful computers.
    Magnons have enormous potential, but they are often difficult to detect without bulky pieces of lab equipment. Such setups are fine for conducting experiments, but not for developing devices, said Columbia researcher Xiaoyang Zhu, such as magnonic devices and so-called spintronics. Seeing magnons can be made much simpler, however, with the right material: a magnetic semiconductor called chromium sulfide bromide (CrSBr) that can be peeled into atom-thin, 2D layers, synthesized in Department of Chemistry professor Xavier Roy’s lab.
    In a new article in Nature, Zhu and collaborators at Columbia, the University of Washington, New York University, and Oak Ridge National Laboratory show that magnons in CrSBr can pair up with another quasiparticle called an exciton, which emits light, offering the researchers a means to “see” the spinning quasiparticle.
    As they perturbed the magnons with light, they observed oscillations from the excitons in the near-infrared range, which is nearly visible to the naked eye. “For the first time, we can see magnons with a simple optical effect,” Zhu said.
    The results may be viewed as quantum transduction, or the conversion of one “quanta” of energy to another, said first author Youn Jun (Eunice) Bae, a postdoc in Zhu’s lab. The energy of excitons is four orders of magnitude larger than that of magnons; now, because they pair together so strongly, we can easily observe tiny changes in the magnons, Bae explained. This transduction may one day enable researchers to build quantum information networks that can take information from spin-based quantum bits — which generally need to be located within millimeters of each other — and convert it to light, a form of energy that can transfer information up to hundreds of miles via optical fibers
    The coherence time — how long the oscillations can last — was also remarkable, Zhu said, lasting much longer than the five-nanosecond limit of the experiment. The phenomenon could travel over seven micrometers and persist even when the CrSBr devices were made of just two atom-thin layers, raising the possibility of building nano-scale spintronic devices. These devices could one day be more efficient alternatives to today’s electronics. Unlike electrons in an electrical current that encounter resistance as they travel, no particles are actually moving in a spin wave.
    The work was supported by Columbia’s NSF-funded Materials Research Science and Engineering Center (MRSEC), with the material created in the DOE-funded Energy Frontier Research Center (EFRC). From here, the researchers plan to explore CrSBr’s quantum information potential, as well as other material candidates. “In the MRSEC and EFRC, we are exploring the quantum properties of several 2D materials that you can stack like papers to create all kinds of new physical phenomena,” Zhu said.
    For example, if magnon-exciton coupling can be found in other kinds of magnetic semiconductors with slightly different properties than CrSBr, they might emit light in a wider range of colors. “We’re assembling the toolbox to construct new devices with customizable properties,” Zhu said.
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    What is the best way to group students? Math model

    Imagine you have a group of 30 children who want to play soccer. You would like to divide them into two teams, so they can practice their skills and learn from their coaches to become better players.
    But what is the most effective way for them to improve: Should you group the children according to skill level, with all of the most skilled players in one group and the rest of the players in the other group? Or, should you divide them into two equal teams by talent and skill?
    For a fresh approach to this age-old question in grouping theory, a researcher from the University of Rochester, along with his childhood friend, an education professor at the University of Nevada, Las Vegas, turned to math.
    “The selection and grouping of individuals for training purposes is extremely common in our society,” says Chad Heatwole, a professor of neurology at the University of Rochester Medical Center and the director of Rochester’s Center for Health + Technology (CHeT). “There is a historic and ongoing rigorous debate regarding the best way to group students for the purpose of instruction.”
    In a paper published in the journal Education Practice and Theory, the research team — which also includes Peter Wiens, an associate professor of teaching and learning at the University of Nevada, Las Vegas, and Christine Zizzi, a director at CHeT — developed, for the first time, a mathematical approach to grouping. The approach compares different grouping methods, selecting the optimal way to group individuals for teacher-led instruction. The research has broad implications in education, as well as in economics, music, medicine, and sports.
    “Our solution was to look at this through a purely mathematical lens, evaluating for the greatest good of the entire sample,” Heatwole says. “To our knowledge, this novel mathematical approach has never been described or utilized in this way.”
    Two approaches in grouping theory More

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    The way you talk to your child about math matters

    This encouraging response may actually do more harm than good to children’s math performance, according to a new study by the University of Georgia.
    Co-conducted by Michael Barger, an assistant professor in the Mary Frances Early College of Education’s Department of Educational Psychology, the study found that encouraging children with responses related to their personal traits or innate abilities may dampen their math motivation and achievement over time.
    Parents who make comments linking their children’s performance to personal attributes like intelligence (e.g., “You’re so smart” or “Math just isn’t your thing”) are using what’s referred to as person responses. In contrast, parents who link their children’s actions, such as effort or strategy use, to their performance (e.g., “You worked hard” or “What might be useful next time you have a math test?”) are using process responses.
    “Person-focused praise sounds good on its face, but ultimately, it might undermine students’ motivation if they run into challenges,” said Barger. “Because if you run into challenges after being told you’re so smart, you might think, ‘Maybe they were wrong.’ We also know that people tend to think about math as something that some people can do and others can’t, and that language is pretty common, whether it’s among parents or teachers, even with young kids.”
    Praising strategy and effort
    For the study, researchers asked more than 500 parents to report on how they respond to their children’s math performance and their math beliefs and goals. Students were assessed in two waves across a year to measure their math motivation and achievement. More

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    Pioneering mathematical formula paves way for exciting advances in health, energy, and food industry

    A groundbreaking mathematical equation has been discovered, which could transform medical procedures, natural gas extraction, and plastic packaging production in the future.
    The new equation, developed by scientists at the University of Bristol, indicates that diffusive movement through permeable material can be modelled exactly for the very first time. It comes a century after world-leading physicists Albert Einstein and Marian von Smoluchowski derived the first diffusion equation and marks important progress in representing motion for a wide range of entities from microscopic particles and natural organisms to human-made devices.
    Until now, scientists looking at particle motion through porous materials such as biological tissues, polymers, various rocks and sponges, have had to rely on approximations or incomplete perspectives.
    The findings, published today in the journal Physical Review Research, provide a novel technique presenting exciting opportunities in a diverse range of settings including health, energy, and the food industry.
    Lead author Toby Kay, who is completing a PhD in Engineering Mathematics, said: “This marks a fundamental step forward since Einstein and Smoluchowski’s studies on diffusion. It revolutionises the modelling of diffusing entities through complex media of all scales, from cellular components and geological compounds to environmental habitats.
    “Previously, mathematical attempts to represent movement through environments scattered with objects that hinder motion, known as permeable barriers, have been limited. By solving this problem, we are paving the way for exciting advances in many different sectors because permeable barriers are routinely encountered by animals, cellular organisms and humans.”
    Creativity in mathematics takes different forms and one of these is the connection between different levels of description of a phenomenon. In this instance, by representing random motion in a microscopic fashion and then subsequently zooming out to describe the process macroscopically, it was possible to find the new equation.
    Further research is needed to apply this mathematical tool to experimental applications, which could improve products and services. For example, being able to model accurately the diffusion of water molecules through biological tissue will advance the interpretation of diffusion-weighted MRI (Magnetic Resonance Imaging) readings. It could also offer more accurate representation of air spreading through food packaging materials, helping to determine shelf life and contamination risk. In addition, quantifying the behaviour of foraging animals interacting with macroscopic barriers, such as fences and roads, could provide better predictions on the consequence of climate change for conservation purposes.
    The use of geolocators, mobile phones, and other sensors has seen the tracking revolution generate movement data of ever-increasing quantity and quality over the past 20 years. This has highlighted the need for more sophisticated modelling tools to represent the movement of wide-ranging entities in their environment, from natural organisms to human-made devices.
    Senior author Dr Luca Giuggioli, Associate Professor in Complexity Sciences at the University of Bristol, said: “This new fundamental equation is another example of the importance of constructing tools and techniques to represent diffusion when space is heterogeneous, that is when the underlying environment changes from location to location.
    “It builds on another long-awaited resolution in 2020 of a mathematical conundrum to describe random movement in confined space. This latest discovery is a further significant step forward in improving our understanding of motion in all its shapes and forms — collectively termed the mathematics of movement — which has many exciting potential applications.”
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    Researchers construct most complex, complete synthetic microbiome

    Key studies in the last decade have shown that the gut microbiome, the collection of hundreds of bacterial species that live in the human digestive system, influences neural development, response to cancer immunotherapies, and other aspects of health. But these communities are complex and without systematic ways to study the constituents, the exact cells and molecules linked with certain diseases remain a mystery.
    Stanford University researchers have built the most complex and well-defined synthetic microbiome, creating a community of over 100 bacterial species that was successfully transplanted into mice. The ability to add, remove, and edit individual species will allow scientists to better understand the links between the microbiome and health, and eventually develop first-in-class microbiome therapies.
    Many key microbiome studies have been done using fecal transplants, which introduce the entire, natural microbiome from one organism to another. While scientists routinely silence a gene or remove a protein from a specific cell or even an entire mouse, there is no such set of tools to remove or modify one species among the hundreds in a given fecal sample.
    “So much of what we know about biology, we wouldn’t know if it weren’t for the ability to manipulate complex biological systems piecewise,” said Michael Fischbach, Institute Scholar at Sarafan ChEM-H and corresponding author on the study, published in Cell on Sept. 6.
    Fischbach, who is an associate professor of bioengineering and of microbiology and immunology, and others saw one solution: Build a microbiome from scratch by growing individually and then mixing its constituent bacteria.
    Building the ark
    Each cell in the microbiome occupies a specific functional niche, performing reactions that break down and build up molecules. To build a microbiome, the team had to ensure that the final mixture was not only stable, maintaining a balance without any single species overpowering the rest, but also functional, performing all the actions of a complete, natural microbiome. Selecting species to include in their synthetic community was also difficult given the natural variation across individuals; two people selected at random share less than half of their microbial genes. More

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    Artificial Intelligence tool could reduce common drug side effects

    Research led by the University of Exeter and Kent and Medway NHS and Social Care Partnership Trust, published in Age and Ageing, assessed a new tool designed to calculate which medicines are more likely to experience adverse anticholinergic effects on the body and brain. These complications can occur from many -prescription and over-the-counter drugs which affects the brain by blocking a key neurotransmitter called acetylcholine. Many medicines, including some bladder medications, anti-depressants, medications for stomach and Parkinson’s disease have some degree of anticholinergic effect. They are commonly taken by older people.
    Anticholinergic side effects include confusion, blurred vision, dizziness, falls and a decline in brain function. Anticholinergic effects may also increase risks of falls and may be associated with an increase in mortality. They have also been linked to a higher risk of dementia when used long term.
    Now, researchers have developed a tool to calculate harmful effects of medicines using artificial intelligence. The team created a new online tool, International Anticholinergic Cognitive Burden Tool (IACT), is uses natural language processing which is an artificial intelligence methdolody and chemical structure analysis to identify medications that have anticholinergic effect.
    The tool is the first to incorporate a machine learning technique, to develop an automatically updated tool available on a website portal. The anticholinergic burden is assessed by assigning a score based on reported adverse events and aligning closely with the chemical structure of the drug being considered for prescription, resulting in a more accurate and up-to-date scoring system than any previous system. Ultimately, after further research and modelling with real world patient data the tool developed could help to support prescribing reducing risks form common medicines.
    Professor Chris Fox, at the University of Exeter, is one of the study authors. He said:: “Use of medicines with anticholinergic effects can have significant harmful effects for example falls and confusion which are avoidable, we urgently need to reduce the harmful side effects as this can leads to hospitalisation and death. This new tool provides a promising avenue towards a more tailored personalised medicine approach, of ensuring the right person gets a safe and effective treatment whilst avoiding unwanted anticholinergic effects.”
    The team surveyed 110 health professionals, including pharmacists and prescribing nurses. Of this group, 85 per cent said they would use a tool to assess risk of anticholinergic side effects, if available. The team also gathered usability feedback to help improve the tool further.
    Dr Saber Sami, at the University of East Anglia, said: “Our tool is the first to use innovative artificial intelligence technology in measures of anticholinergic burden — ultimately, once further research has been conducted the tool should support pharmacists and prescribing health professionals in finding the best treatment for patients.”
    Professor Ian Maidment, from Aston University, said: “I have been working in this area for over 20 years. Anti-cholinergic side-effects can be very debilitating for patients. We need better ways to assess these side-effects.”
    The research team includes collaboration with AKFA University Medical School, Uzbekistan, and the Universities of East Anglia, Aston, Kent and Aberdeen. They aim to continue development of the tool with the aim that it can be deployed in day-to-day practice which this study supports.
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    A coral pollution study unexpectedly helped explain Hurricane Maria’s fury

    Hurricane Maria struck the island of Puerto Rico early on September 20, 2017, with 250-kilometer-per-hour winds, torrential rains and a storm surge up to three meters high. In its wake: nearly 3,000 people dead, an almost yearlong power outage and over $90 billion in damages to homes, businesses and essential infrastructure, including roads and bridges.

    Geologist and diver Milton Carlo took shelter at his house in Cabo Rojo on the southwest corner of the island with his wife, daughter and infant grandson. He watched the raging winds of the Category 4 hurricane lift his neighbor’s SUV into the air, and remembers those hours as some of the worst of his life.

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    For weeks, the rest of the world was in the dark about the full extent of the devastation, because Maria had destroyed the island’s main weather radar and almost all cell phone towers.

    Far away on the U.S. West Coast, in Santa Cruz, Calif., oceanographer Olivia Cheriton watched satellite radar images of Maria passing over the instruments she and her U.S. Geological Survey team had anchored a few kilometers southwest of Puerto Rico. The instruments, placed offshore from the seaside town of La Parguera, were there to track pollution circulating around some of the island’s endangered corals.

    More than half a year went by before she learned the improbable fate of those instruments: They had survived and had captured data revealing hurricane-related ocean dynamics that no scientist had ever recorded.

    The wind-driven coastal currents interacted with the seafloor in a way that prevented Maria from drawing cold water from the depths of the sea up to the surface. The sea surface stayed as warm as bathwater. Heat is a hurricane’s fuel source, so a warmer sea surface leads to a more intense storm. As Cheriton figured out later, the phenomenon she stumbled upon likely played a role in maintaining Maria’s Category 4 status as it raked Puerto Rico for eight hours.

    “There was absolutely no plan to capture the impact of a storm like Maria,” Cheriton says. “In fact, if we somehow could’ve known that a storm like that was going to occur, we wouldn’t have put hundreds of thousands of dollars’ worth of scientific instrumentation in the water.”

    A storm’s path is guided by readily observable, large-scale atmospheric features such as trade winds and high-pressure zones. Its intensity, on the other hand, is driven by weather events inside the hurricane and wave action deep below the ocean’s surface. The findings by Cheriton and colleagues, published May 2021 in Science Advances, help explain why hurricanes often get stronger before making landfall and can therefore help forecasters make more accurate predictions.

    Reef pollution

    Cheriton’s original research objective was to figure out how sea currents transport polluted sediments from Guánica Bay — where the Lajas Valley drains into the Caribbean Sea — to the pristine marine ecosystems 10 kilometers west in La Parguera Natural Reserve, famous for its bioluminescent waters.

    Endangered elkhorn and mountainous star corals, called “the poster children of Caribbean reef decline” by marine geologist Clark Sherman, live near shore in some of the world’s highest recorded concentrations of now-banned industrial chemicals. Those polychlorinated biphenyls, or PCBs, hinder coral reproduction, growth, feeding and defensive responses, says Sherman, of the University of Puerto Rico–Mayagüez.

    Elkhorn coral (left) and mountainous star coral (right) were once ubiquitous in the Caribbean. Their numbers have dropped greatly due to bleaching and disease. Pollution is partly to blame.  FROM LEFT: NICK HOBGOOD/WIKIMEDIA COMMONS (CC BY-SA 3.0); NOAA FISHERIES

    Half of corals in the Caribbean have died since monitoring began in the 1970s, and pollution is a major cause, according to an April 2020 study in Science Advances. Of particular interest to Cheriton, Sherman and their colleagues was whether the pollution had reached deepwater, or mesophotic, reefs farther offshore, which could be a refuge for coral species that were known to be dying in shallower areas.

    The main artery for this pollution is the Rio Loco — which translates to “Crazy River.” It spews a toxic runoff of eroded sediments from the Lajas Valley’s dirt roads and coffee plantations into Guánica Bay, which supports a vibrant fishing community. Other possible contributors to the pollution — oil spills, a fertilizer plant, sewage and now-defunct sugar mills — are the subject of investigations by public health researchers and the U.S. Environmental Protection Agency.

    In June 2017, the team convened in La Parguera to install underwater sensors to measure and track the currents in this threatened marine environment. From Sherman’s lab on a tiny islet overrun with iguanas the size of house cats, he and Cheriton, along with team leader and USGS research geologist Curt Storlazzi and USGS physical scientist Joshua Logan, launched a boat into choppy seas.

    Marine geologist Clark Sherman dives amid colonies of healthy great star corals, black corals, a large sea fan and a variety of sponges along the steep island shelf of southwest Puerto Rico. Sherman helped investigate whether pollution was reaching these deepwater reefs.E. TUOHY/UNIV. OF PUERTO RICO–MAYAGÜEZ

    At six sites near shore, Storlazzi, Sherman and Logan dove to the seafloor and used epoxy to anchor pressure gauges and batonlike current meters. Together the instruments measured hourly temperature, wave height and current speed. The team then moved farther offshore where the steep island shelf drops off at a 45-degree angle to a depth of 60 meters, but the heavy ocean chop scuttled their efforts to install instruments there.

    In June 2017, research geologist Curt Storlazzi (left) and physical scientist Joshua Logan (right) prepare to dive near Puerto Rico’s Guánica Bay to install instruments for monitoring currents suspected of delivering pollution to coral reefs.USGS

    For help working in the difficult conditions, Sherman enlisted two expert divers for a second attempt: Carlo, the geologist and diving safety officer, and marine scientist Evan Tuohy, both of the University of Puerto Rico–­Mayagüez. The two were able to install the most important and largest piece, a hydroacoustic instrument comprising several drums fastened to a metal grid, which tracked the direction and speed of currents every minute using pulsating sound waves. A canister containing temperature and salinity sensors took readings every two minutes. Above this equipment, an electric thermometer extended to within 12 meters of the surface, registering temperature every five meters vertically every few seconds.

    The instruments installed by Storlazzi, Logan and others collected unexpected underwater ocean observations during Hurricane Maria. An acoustic Doppler current profiler (left) used pulsating sound waves to measure the direction and speed of currents at the shelf break and slope site about 12 kilometers offshore of La Parguera. A Marotte current meter (right) measured wave height, current speed and temperature at six spots close to shore.USGS

    Working in concert, the instruments gave a high-resolution, seafloor-to-surface snapshot of the ocean’s hydrodynamics on a near-continuous basis. The equipment had to sit level on the sloping seafloor so as not to skew the measurements and remain firmly in place. Little did the researchers know that the instruments would soon be battered by one of the most destructive storms in history.

    Becoming Maria

    The word hurricane derives from the Caribbean Taino people’s Huricán, god of evil. Some of the strongest of these Atlantic tropical cyclones begin where scorching winds from the Sahara clash with moist subtropical air over the island nation of Cape Verde off western Africa. The worst of these atmospheric disturbances create severe thunderstorms with giant cumulonimbus clouds that flatten out against the stratosphere. Propelled by the Earth’s rotation, they begin to circle counterclockwise around each other — a phenomenon known as the Coriolis effect.

    Weather conditions that summer had already spawned two monster hurricanes: Harvey and Irma. By late September, the extremely warm sea surface — 29º Celsius or hotter in some places — gave up its heat energy by way of evaporation into Maria’s rushing winds. All hurricanes begin as an area of low pressure, which in turn sucks in more wind, accelerating the rise of hot air, or convection. Countervailing winds known as shear can sometimes topple the cone of moist air spiraling upward. But that didn’t happen, so Maria continued to grow in size and intensity.

    Meteorologists hoped that Maria would lose force as it moved across the Caribbean, weakened by the wake of cooler water Irma had churned up two weeks earlier. Instead, Maria tracked south, steaming toward the eastern Caribbean island of Dominica. Within 15 hours of making landfall, its maximum sustained wind speed doubled, reaching a house-leveling 260 kilometers per hour. That doubling intensified the storm from a milder (still dangerous) Category 1 to a strong Category 5.

    NOAA’s computer forecasting models did not anticipate such rapid intensification. Irma had also raged with unforeseen intensity.

    After striking Dominica hard, Maria’s eyewall broke down, replaced by an outer band of whipping thunderstorms. This slightly weakened Maria to 250 kilometers per hour before it hit Puerto Rico, while expanding the diameter of the storm’s eyewall — the area of strong winds and heaviest precipitation — to 52 kilometers. That’s close to the width of the island.

    Hurricane Maria made landfall on Puerto Rico early in the morning on September 20, 2017, and cut across the island diagonally toward the northwest. Its eyewall generated maximum sustained winds of  250 kilometers per hour and spanned almost the width of the island.CIRA/NOAA

    It’s still not fully understood why Maria had suddenly gone berserk. Various theories point to the influence of hot towers — convective bursts of heat energy from thunderclouds that punch up into the stratosphere — or deep warm pools, buoyant freshwater eddies spilling out of the Amazon and Orinoco rivers into the Atlantic, where currents carry these pockets of hurricane-fueling heat to the Gulf of Mexico and the Caribbean Sea.

    But even though these smaller-scale events may have a big impact on intensity, they aren’t fully accounted for in weather models, says Hua Leighton, a scientist at the National Oceanic and Atmospheric Administration’s hurricane research division and the University of Miami’s Cooperative Institute for Marine and Atmospheric Studies. Leighton develops forecasting models and investigates rapid intensification of hurricanes.

    “We cannot measure everything in the atmosphere,” Leighton says.

    Without accurate data on all the factors that drive hurricane intensity, computer models can’t easily predict when the catalyzing events will occur, she says. Nor can models account for everything that happens inside the ocean during a hurricane. They don’t have the data.

    Positioning instruments just before a hurricane hits is a major challenge. But NOAA is making progress. It has launched a new generation of hurricane weather buoys in the western North Atlantic and remote control surface sensors called Saildrones that examine the air-sea interface between hurricanes and the ocean (SN: 6/8/19, p. 24).

    Underwater, NOAA uses other drones, or gliders, to profile the vast areas regularly traversed by tropical storms. These gliders collected 13,200 temperature and salinity readings in 2020. By contrast, the instruments that the team set in Puerto Rico’s waters in 2017 collected over 250 million data points, including current velocity and direction — a rare and especially valuable glimpse of hurricane-induced ocean dynamics at a single location.

    A different view

    After the storm passed, Storlazzi was sure the hurricane had destroyed his instruments. They weren’t designed to take that kind of punishment. The devices generally work in much calmer conditions, not the massive swells generated by Maria, which could increase water pressure to a level that would almost certainly crush instrument sensors.

    But remarkably, the instruments were battered but not lost. Sherman, Carlo and Touhy retrieved them after Maria passed and put them in crates awaiting the research group’s return.

    Milton Carlo (left) and Evan Tuohy (right), shown in an earlier deepwater dive, helped  place the current-monitoring instruments at the hard-to-reach sites where hurricane data were collected.MIKE ECHEVARRIA

    When Storlazzi and USGS oceanographer Kurt Rosenberger pried open the instrument casings in January 2018, no water gushed out. Good sign. The electronics appeared intact. And the lithium batteries had powered the rapid-fire sampling enterprise for the entire six-month duration. The researchers quickly downloaded a flood of data, backed it up and started transmitting it to Cheriton, who began sending back plots and graphs of what the readings showed.

    Floodwaters from the massive rains brought by Maria had pushed a whole lot of polluted sediment to the reefs outside Guánica Bay, spiking PCB concentrations and threatening coral health. As of a few months after the storm, the pollution hadn’t reached the deeper reefs.

    Then the researchers realized that their data told another story: what happens underwater during a massive hurricane. They presumed that other researchers had previously captured a profile of the churning ocean depths beneath a hurricane at the edge of a tropical island.

    Remarkably, that was not the case.

    “Nobody’s even measured this, let alone reported it in any published literature,” Cheriton says. The team began to explore the hurricane data not knowing where it might lead.

    “What am I looking at here?” Cheriton kept asking herself as she plotted and analyzed temperature, current velocity and salinity values using computer algorithms. The temperature gradient that showed the ocean’s internal or underwater waves was different than anything she’d seen before.

    Oceanographer Olivia Cheriton realized that data on ocean currents told a new story about Hurricane Maria.O.M. CHERITON

    During the hurricane, the top 20 meters of the Caribbean Sea had consistently remained at or above 26º C, a few degrees warmer than the layers beneath. But the surface waters should have been cooled if, as expected, Maria’s winds had acted like a big spoon, mixing the warm surface with cold water stirred up from the seafloor 50 to 80 meters below. Normally, the cooler surface temperature restricts the heat supply, weakening the hurricane. But the cold water wasn’t reaching the surface.

    To try to make sense of what she was seeing, Cheriton imagined herself inside the data, in a protective bubble on the seafloor with the instruments as Maria swept over. Storlazzi worked alongside her analyzing the data, but focused on the sediments circulating around the coral reefs.

    Cheriton was listening to “An Awesome Wave” by indie-pop band Alt-J and getting goosebumps while the data swirled before them. Drawing on instincts from her undergraduate astronomy training, she focused her mind’s eye on a constellation of data overhead and told Storlazzi to do the same.

    “Look up Curt!” she said.

    Up at the crest of the island shelf, where the seafloor drops off, the current velocity data revealed a broad stream of water gushing from the shore at almost 1 meter per second, as if from a fire hose. Several hours before Maria arrived, the wind-driven current had reversed direction and was now moving an order of magnitude faster. The rushing surface water thus became a barrier, trapping the cold water beneath it.

    As a result, the surface stayed warm, increasing the force of the hurricane. The cooler layers below then started to pile up vertically into distinct layers, one on top of the other, beneath the gushing waters above.

    Cheriton calculated that with the fire hose phenomenon the contribution from coastal waters in this area to Maria’s intensity was, on average, 65 percent greater, compared with what it would have been otherwise.

    Oceanographer Travis Miles of Rutgers University in New Brunswick, N.J., who was not involved in the research, calls Cheriton and the team’s work a “frontier study” that draws researchers’ attention to near-shore processes. Miles can relate to Cheriton and her team’s accidental hurricane discovery from personal experience: When his water quality–sampling gliders wandered into Hurricane Irene’s path in 2011, they revealed that the ocean off the Jersey Shore had cooled in front of the storm. Irene’s onshore winds had induced seawater mixing across the broad continental shelf and lowered sea surface temperatures.

    The Puerto Rico data show that offshore winds over a steep island shelf produced the opposite effect and should help researchers better understand storm-induced mixing of coastal areas, says NOAA senior scientist Hyun-Sook Kim, who was not involved in the research. It can help with identifying deficiencies in the computer models she relies on when providing guidance to storm-tracking meteorologists at the National Hurricane Center in Miami and the Joint Typhoon Warning Center in Hawaii.

    And the unexpected findings also could help scientists get a better handle on coral reefs and the role they play in protecting coastlines. “The more we study the ocean, especially close to the coast,” Carlo says, “the more we can improve conditions for the coral and the people living on the island.” More