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    An AI dataset carves new paths to tornado detection

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    The return of spring in the Northern Hemisphere touches off tornado season. A tornado’s twisting funnel of dust and debris seems an unmistakable sight. But that sight can be obscured to radar, the tool of meteorologists. It’s hard to know exactly when a tornado has formed, or even why.

    A new dataset could hold answers. It contains radar returns from thousands of tornadoes that have hit the United States in the past 10 years. Storms that spawned tornadoes are flanked by other severe storms, some with nearly identical conditions, that never did. MIT Lincoln Laboratory researchers who curated the dataset, called TorNet, have now released it open source. They hope to enable breakthroughs in detecting one of nature’s most mysterious and violent phenomena.

    “A lot of progress is driven by easily available, benchmark datasets. We hope TorNet will lay a foundation for machine learning algorithms to both detect and predict tornadoes,” says Mark Veillette, the project’s co-principal investigator with James Kurdzo. Both researchers work in the Air Traffic Control Systems Group. 

    Along with the dataset, the team is releasing models trained on it. The models show promise for machine learning’s ability to spot a twister. Building on this work could open new frontiers for forecasters, helping them provide more accurate warnings that might save lives. 

    Swirling uncertainty

    About 1,200 tornadoes occur in the United States every year, causing millions to billions of dollars in economic damage and claiming 71 lives on average. Last year, one unusually long-lasting tornado killed 17 people and injured at least 165 others along a 59-mile path in Mississippi.  

    Yet tornadoes are notoriously difficult to forecast because scientists don’t have a clear picture of why they form. “We can see two storms that look identical, and one will produce a tornado and one won’t. We don’t fully understand it,” Kurdzo says.

    A tornado’s basic ingredients are thunderstorms with instability caused by rapidly rising warm air and wind shear that causes rotation. Weather radar is the primary tool used to monitor these conditions. But tornadoes lay too low to be detected, even when moderately close to the radar. As the radar beam with a given tilt angle travels further from the antenna, it gets higher above the ground, mostly seeing reflections from rain and hail carried in the “mesocyclone,” the storm’s broad, rotating updraft. A mesocyclone doesn’t always produce a tornado.

    With this limited view, forecasters must decide whether or not to issue a tornado warning. They often err on the side of caution. As a result, the rate of false alarms for tornado warnings is more than 70 percent. “That can lead to boy-who-cried-wolf syndrome,” Kurdzo says.  

    In recent years, researchers have turned to machine learning to better detect and predict tornadoes. However, raw datasets and models have not always been accessible to the broader community, stifling progress. TorNet is filling this gap.

    The dataset contains more than 200,000 radar images, 13,587 of which depict tornadoes. The rest of the images are non-tornadic, taken from storms in one of two categories: randomly selected severe storms or false-alarm storms (those that led a forecaster to issue a warning but that didn’t produce a tornado).

    Each sample of a storm or tornado comprises two sets of six radar images. The two sets correspond to different radar sweep angles. The six images portray different radar data products, such as reflectivity (showing precipitation intensity) or radial velocity (indicating if winds are moving toward or away from the radar).

    A challenge in curating the dataset was first finding tornadoes. Within the corpus of weather radar data, tornadoes are extremely rare events. The team then had to balance those tornado samples with difficult non-tornado samples. If the dataset were too easy, say by comparing tornadoes to snowstorms, an algorithm trained on the data would likely over-classify storms as tornadic.

    “What’s beautiful about a true benchmark dataset is that we’re all working with the same data, with the same level of difficulty, and can compare results,” Veillette says. “It also makes meteorology more accessible to data scientists, and vice versa. It becomes easier for these two parties to work on a common problem.”

    Both researchers represent the progress that can come from cross-collaboration. Veillette is a mathematician and algorithm developer who has long been fascinated by tornadoes. Kurdzo is a meteorologist by training and a signal processing expert. In grad school, he chased tornadoes with custom-built mobile radars, collecting data to analyze in new ways.

    “This dataset also means that a grad student doesn’t have to spend a year or two building a dataset. They can jump right into their research,” Kurdzo says.

    This project was funded by Lincoln Laboratory’s Climate Change Initiative, which aims to leverage the laboratory’s diverse technical strengths to help address climate problems threatening human health and global security.

    Chasing answers with deep learning

    Using the dataset, the researchers developed baseline artificial intelligence (AI) models. They were particularly eager to apply deep learning, a form of machine learning that excels at processing visual data. On its own, deep learning can extract features (key observations that an algorithm uses to make a decision) from images across a dataset. Other machine learning approaches require humans to first manually label features. 

    “We wanted to see if deep learning could rediscover what people normally look for in tornadoes and even identify new things that typically aren’t searched for by forecasters,” Veillette says.

    The results are promising. Their deep learning model performed similar to or better than all tornado-detecting algorithms known in literature. The trained algorithm correctly classified 50 percent of weaker EF-1 tornadoes and over 85 percent of tornadoes rated EF-2 or higher, which make up the most devastating and costly occurrences of these storms.

    They also evaluated two other types of machine-learning models, and one traditional model to compare against. The source code and parameters of all these models are freely available. The models and dataset are also described in a paper submitted to a journal of the American Meteorological Society (AMS). Veillette presented this work at the AMS Annual Meeting in January.

    “The biggest reason for putting our models out there is for the community to improve upon them and do other great things,” Kurdzo says. “The best solution could be a deep learning model, or someone might find that a non-deep learning model is actually better.”

    TorNet could be useful in the weather community for others uses too, such as for conducting large-scale case studies on storms. It could also be augmented with other data sources, like satellite imagery or lightning maps. Fusing multiple types of data could improve the accuracy of machine learning models.

    Taking steps toward operations

    On top of detecting tornadoes, Kurdzo hopes that models might help unravel the science of why they form.

    “As scientists, we see all these precursors to tornadoes — an increase in low-level rotation, a hook echo in reflectivity data, specific differential phase (KDP) foot and differential reflectivity (ZDR) arcs. But how do they all go together? And are there physical manifestations we don’t know about?” he asks.

    Teasing out those answers might be possible with explainable AI. Explainable AI refers to methods that allow a model to provide its reasoning, in a format understandable to humans, of why it came to a certain decision. In this case, these explanations might reveal physical processes that happen before tornadoes. This knowledge could help train forecasters, and models, to recognize the signs sooner. 

    “None of this technology is ever meant to replace a forecaster. But perhaps someday it could guide forecasters’ eyes in complex situations, and give a visual warning to an area predicted to have tornadic activity,” Kurdzo says.

    Such assistance could be especially useful as radar technology improves and future networks potentially grow denser. Data refresh rates in a next-generation radar network are expected to increase from every five minutes to approximately one minute, perhaps faster than forecasters can interpret the new information. Because deep learning can process huge amounts of data quickly, it could be well-suited for monitoring radar returns in real time, alongside humans. Tornadoes can form and disappear in minutes.

    But the path to an operational algorithm is a long road, especially in safety-critical situations, Veillette says. “I think the forecaster community is still, understandably, skeptical of machine learning. One way to establish trust and transparency is to have public benchmark datasets like this one. It’s a first step.”

    The next steps, the team hopes, will be taken by researchers across the world who are inspired by the dataset and energized to build their own algorithms. Those algorithms will in turn go into test beds, where they’ll eventually be shown to forecasters, to start a process of transitioning into operations.

    In the end, the path could circle back to trust.

    “We may never get more than a 10- to 15-minute tornado warning using these tools. But if we could lower the false-alarm rate, we could start to make headway with public perception,” Kurdzo says. “People are going to use those warnings to take the action they need to save their lives.” More

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      in Environment

      MIT-derived algorithm helps forecast the frequency of extreme weather

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      To assess a community’s risk of extreme weather, policymakers rely first on global climate models that can be run decades, and even centuries, forward in time, but only at a coarse resolution. These models might be used to gauge, for instance, future climate conditions for the northeastern U.S., but not specifically for Boston.

      To estimate Boston’s future risk of extreme weather such as flooding, policymakers can combine a coarse model’s large-scale predictions with a finer-resolution model, tuned to estimate how often Boston is likely to experience damaging floods as the climate warms. But this risk analysis is only as accurate as the predictions from that first, coarser climate model.

      “If you get those wrong for large-scale environments, then you miss everything in terms of what extreme events will look like at smaller scales, such as over individual cities,” says Themistoklis Sapsis, the William I. Koch Professor and director of the Center for Ocean Engineering in MIT’s Department of Mechanical Engineering.

      Sapsis and his colleagues have now developed a method to “correct” the predictions from coarse climate models. By combining machine learning with dynamical systems theory, the team’s approach “nudges” a climate model’s simulations into more realistic patterns over large scales. When paired with smaller-scale models to predict specific weather events such as tropical cyclones or floods, the team’s approach produced more accurate predictions for how often specific locations will experience those events over the next few decades, compared to predictions made without the correction scheme.

      Play video

      This animation shows the evolution of storms around the northern hemisphere, as a result of a high-resolution storm model, combined with the MIT team’s corrected global climate model. The simulation improves the modeling of extreme values for wind, temperature, and humidity, which typically have significant errors in coarse scale models. Credit: Courtesy of Ruby Leung and Shixuan Zhang, PNNL

      Sapsis says the new correction scheme is general in form and can be applied to any global climate model. Once corrected, the models can help to determine where and how often extreme weather will strike as global temperatures rise over the coming years. 

      “Climate change will have an effect on every aspect of human life, and every type of life on the planet, from biodiversity to food security to the economy,” Sapsis says. “If we have capabilities to know accurately how extreme weather will change, especially over specific locations, it can make a lot of difference in terms of preparation and doing the right engineering to come up with solutions. This is the method that can open the way to do that.”

      The team’s results appear today in the Journal of Advances in Modeling Earth Systems. The study’s MIT co-authors include postdoc Benedikt Barthel Sorensen and Alexis-Tzianni Charalampopoulos SM ’19, PhD ’23, with Shixuan Zhang, Bryce Harrop, and Ruby Leung of the Pacific Northwest National Laboratory in Washington state.

      Over the hood

      Today’s large-scale climate models simulate weather features such as the average temperature, humidity, and precipitation around the world, on a grid-by-grid basis. Running simulations of these models takes enormous computing power, and in order to simulate how weather features will interact and evolve over periods of decades or longer, models average out features every 100 kilometers or so.

      “It’s a very heavy computation requiring supercomputers,” Sapsis notes. “But these models still do not resolve very important processes like clouds or storms, which occur over smaller scales of a kilometer or less.”

      To improve the resolution of these coarse climate models, scientists typically have gone under the hood to try and fix a model’s underlying dynamical equations, which describe how phenomena in the atmosphere and oceans should physically interact.

      “People have tried to dissect into climate model codes that have been developed over the last 20 to 30 years, which is a nightmare, because you can lose a lot of stability in your simulation,” Sapsis explains. “What we’re doing is a completely different approach, in that we’re not trying to correct the equations but instead correct the model’s output.”

      The team’s new approach takes a model’s output, or simulation, and overlays an algorithm that nudges the simulation toward something that more closely represents real-world conditions. The algorithm is based on a machine-learning scheme that takes in data, such as past information for temperature and humidity around the world, and learns associations within the data that represent fundamental dynamics among weather features. The algorithm then uses these learned associations to correct a model’s predictions.

      “What we’re doing is trying to correct dynamics, as in how an extreme weather feature, such as the windspeeds during a Hurricane Sandy event, will look like in the coarse model, versus in reality,” Sapsis says. “The method learns dynamics, and dynamics are universal. Having the correct dynamics eventually leads to correct statistics, for example, frequency of rare extreme events.”

      Climate correction

      As a first test of their new approach, the team used the machine-learning scheme to correct simulations produced by the Energy Exascale Earth System Model (E3SM), a climate model run by the U.S. Department of Energy, that simulates climate patterns around the world at a resolution of 110 kilometers. The researchers used eight years of past data for temperature, humidity, and wind speed to train their new algorithm, which learned dynamical associations between the measured weather features and the E3SM model. They then ran the climate model forward in time for about 36 years and applied the trained algorithm to the model’s simulations. They found that the corrected version produced climate patterns that more closely matched real-world observations from the last 36 years, not used for training.

      “We’re not talking about huge differences in absolute terms,” Sapsis says. “An extreme event in the uncorrected simulation might be 105 degrees Fahrenheit, versus 115 degrees with our corrections. But for humans experiencing this, that is a big difference.”

      When the team then paired the corrected coarse model with a specific, finer-resolution model of tropical cyclones, they found the approach accurately reproduced the frequency of extreme storms in specific locations around the world.

      “We now have a coarse model that can get you the right frequency of events, for the present climate. It’s much more improved,” Sapsis says. “Once we correct the dynamics, this is a relevant correction, even when you have a different average global temperature, and it can be used for understanding how forest fires, flooding events, and heat waves will look in a future climate. Our ongoing work is focusing on analyzing future climate scenarios.”

      “The results are particularly impressive as the method shows promising results on E3SM, a state-of-the-art climate model,” says Pedram Hassanzadeh, an associate professor who leads the Climate Extremes Theory and Data group at the University of Chicago and was not involved with the study. “It would be interesting to see what climate change projections this framework yields once future greenhouse-gas emission scenarios are incorporated.”

      This work was supported, in part, by the U.S. Defense Advanced Research Projects Agency. More

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      in Environment

      Artificial reef designed by MIT engineers could protect marine life, reduce storm damage

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      The beautiful, gnarled, nooked-and-crannied reefs that surround tropical islands serve as a marine refuge and natural buffer against stormy seas. But as the effects of climate change bleach and break down coral reefs around the world, and extreme weather events become more common, coastal communities are left increasingly vulnerable to frequent flooding and erosion.

      An MIT team is now hoping to fortify coastlines with “architected” reefs — sustainable, offshore structures engineered to mimic the wave-buffering effects of natural reefs while also providing pockets for fish and other marine life.

      The team’s reef design centers on a cylindrical structure surrounded by four rudder-like slats. The engineers found that when this structure stands up against a wave, it efficiently breaks the wave into turbulent jets that ultimately dissipate most of the wave’s total energy. The team has calculated that the new design could reduce as much wave energy as existing artificial reefs, using 10 times less material.

      The researchers plan to fabricate each cylindrical structure from sustainable cement, which they would mold in a pattern of “voxels” that could be automatically assembled, and would provide pockets for fish to explore and other marine life to settle in. The cylinders could be connected to form a long, semipermeable wall, which the engineers could erect along a coastline, about half a mile from shore. Based on the team’s initial experiments with lab-scale prototypes, the architected reef could reduce the energy of incoming waves by more than 95 percent.

      “This would be like a long wave-breaker,” says Michael Triantafyllou, the Henry L. and Grace Doherty Professor in Ocean Science and Engineering in the Department of Mechanical Engineering. “If waves are 6 meters high coming toward this reef structure, they would be ultimately less than a meter high on the other side. So, this kills the impact of the waves, which could prevent erosion and flooding.”

      Details of the architected reef design are reported today in a study appearing in the open-access journal PNAS Nexus. Triantafyllou’s MIT co-authors are Edvard Ronglan SM ’23; graduate students Alfonso Parra Rubio, Jose del Auila Ferrandis, and Erik Strand; research scientists Patricia Maria Stathatou and Carolina Bastidas; and Professor Neil Gershenfeld, director of the Center for Bits and Atoms; along with Alexis Oliveira Da Silva at the Polytechnic Institute of Paris, Dixia Fan of Westlake University, and Jeffrey Gair Jr. of Scinetics, Inc.

      Leveraging turbulence

      Some regions have already erected artificial reefs to protect their coastlines from encroaching storms. These structures are typically sunken ships, retired oil and gas platforms, and even assembled configurations of concrete, metal, tires, and stones. However, there’s variability in the types of artificial reefs that are currently in place, and no standard for engineering such structures. What’s more, the designs that are deployed tend to have a low wave dissipation per unit volume of material used. That is, it takes a huge amount of material to break enough wave energy to adequately protect coastal communities.

      The MIT team instead looked for ways to engineer an artificial reef that would efficiently dissipate wave energy with less material, while also providing a refuge for fish living along any vulnerable coast.

      “Remember, natural coral reefs are only found in tropical waters,” says Triantafyllou, who is director of the MIT Sea Grant. “We cannot have these reefs, for instance, in Massachusetts. But architected reefs don’t depend on temperature, so they can be placed in any water, to protect more coastal areas.”

      MIT researchers test the wave-breaking performance of two artificial reef structures in the MIT Towing Tank.Credit: Courtesy of the researchers

      The new effort is the result of a collaboration between researchers in MIT Sea Grant, who developed the reef structure’s hydrodynamic design, and researchers at the Center for Bits and Atoms (CBA), who worked to make the structure modular and easy to fabricate on location. The team’s architected reef design grew out of two seemingly unrelated problems. CBA researchers were developing ultralight cellular structures for the aerospace industry, while Sea Grant researchers were assessing the performance of blowout preventers in offshore oil structures — cylindrical valves that are used to seal off oil and gas wells and prevent them from leaking.

      The team’s tests showed that the structure’s cylindrical arrangement generated a high amount of drag. In other words, the structure appeared to be especially efficient in dissipating high-force flows of oil and gas. They wondered: Could the same arrangement dissipate another type of flow, in ocean waves?

      The researchers began to play with the general structure in simulations of water flow, tweaking its dimensions and adding certain elements to see whether and how waves changed as they crashed against each simulated design. This iterative process ultimately landed on an optimized geometry: a vertical cylinder flanked by four long slats, each attached to the cylinder in a way that leaves space for water to flow through the resulting structure. They found this setup essentially breaks up any incoming wave energy, causing parts of the wave-induced flow to spiral to the sides rather than crashing ahead.

      “We’re leveraging this turbulence and these powerful jets to ultimately dissipate wave energy,” Ferrandis says.

      Standing up to storms

      Once the researchers identified an optimal wave-dissipating structure, they fabricated a laboratory-scale version of an architected reef made from a series of the cylindrical structures, which they 3D-printed from plastic. Each test cylinder measured about 1 foot wide and 4 feet tall. They assembled a number of cylinders, each spaced about a foot apart, to form a fence-like structure, which they then lowered into a wave tank at MIT. They then generated waves of various heights and measured them before and after passing through the architected reef.

      “We saw the waves reduce substantially, as the reef destroyed their energy,” Triantafyllou says.

      The team has also looked into making the structures more porous, and friendly to fish. They found that, rather than making each structure from a solid slab of plastic, they could use a more affordable and sustainable type of cement.

      “We’ve worked with biologists to test the cement we intend to use, and it’s benign to fish, and ready to go,” he adds.

      They identified an ideal pattern of “voxels,” or microstructures, that cement could be molded into, in order to fabricate the reefs while creating pockets in which fish could live. This voxel geometry resembles individual egg cartons, stacked end to end, and appears to not affect the structure’s overall wave-dissipating power.

      “These voxels still maintain a big drag while allowing fish to move inside,” Ferrandis says.

      The team is currently fabricating cement voxel structures and assembling them into a lab-scale architected reef, which they will test under various wave conditions. They envision that the voxel design could be modular, and scalable to any desired size, and easy to transport and install in various offshore locations. “Now we’re simulating actual sea patterns, and testing how these models will perform when we eventually have to deploy them,” says Anjali Sinha, a graduate student at MIT who recently joined the group.

      Going forward, the team hopes to work with beach towns in Massachusetts to test the structures on a pilot scale.

      “These test structures would not be small,” Triantafyllou emphasizes. “They would be about a mile long, and about 5 meters tall, and would cost something like 6 million dollars per mile. So it’s not cheap. But it could prevent billions of dollars in storm damage. And with climate change, protecting the coasts will become a big issue.”

      This work was funded, in part, by the U.S. Defense Advanced Research Projects Agency. More

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      in Energy

      Lessons from Fukushima: Prepare for the unlikely

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      When a devastating earthquake and tsunami overwhelmed the protective systems at the Fukushima Dai’ichi nuclear power plant complex in Japan in March 2011, it triggered a sequence of events leading to one of the worst releases of radioactive materials in the world to date. Although nuclear energy is having a revival as a low-emissions energy source to mitigate climate change, the Fukushima accident is still cited as a reason for hesitancy in adopting it.

      A new study synthesizes information from multidisciplinary sources to understand how the Fukushima Dai’ichi disaster unfolded, and points to the importance of mitigation measures and last lines of defense — even against accidents considered highly unlikely. These procedures have received relatively little attention, but they are critical in determining how severe the consequences of a reactor failure will be, the researchers say.

      The researchers note that their synthesis is one of the few attempts to look at data across disciplinary boundaries, including: the physics and engineering of what took place within the plant’s systems, the plant operators’ actions throughout the emergency, actions by emergency responders, the meteorology of radionuclide releases and transport, and the environmental and health consequences documented since the event.

      The study appears in the journal iScience, in an open-access paper by postdoc Ali Ayoub and Professor Haruko Wainwright at MIT, along with others in Switzerland, Japan, and New Mexico.

      Since 2013, Wainwright has been leading the research to integrate all the radiation monitoring data in the Fukushima region into integrated maps. “I was staring at the contamination map for nearly 10 years, wondering what created the main plume extending in the northwest direction, but I could not find exact information,” Wainwright says. “Our study is unique because we started from the consequence, the contamination map, and tried to identify the key factors for the consequence. Other people study the Fukushima accident from the root cause, the tsunami.”

      One thing they found was that while all the operating reactors, units 1, 2, and 3, suffered core meltdowns as a result of the failure of emergency cooling systems, units 1 and 3 — although they did experience hydrogen explosions — did not release as much radiation to the environment because their venting systems essentially worked to relieve pressure inside the containment vessels as intended. But the same system in unit 2 failed badly.

      “People think that the hydrogen explosion or the core meltdown were the worst things, or the major driver of the radiological consequences of the accident,” Wainright says, “but our analysis found that’s not the case.” Much more significant in terms of the radiological release was the failure of the one venting mechanism.

      “There is a pressure-release mechanism that goes through water where a lot of the radionuclides get filtered out,” she explains. That system was effective in units 1 and 3, filtering out more than 90 percent of the radioactive elements before the gas was vented. However, “in unit 2, that pressure release mechanism got stuck, and the operators could not manually open it.” A hydrogen explosion in unit 1 had damaged the pressure relief mechanism of unit 2. This led to a breach of the containment structure and direct, unfiltered venting to the atmosphere, which, according to the new study, was what produced the greatest amount of contamination from the whole weeks-long event.

      Another factor was the timing of the attempt to vent the pressure buildup in the reactor. Guidelines at the time, and to this day in many reactors, specified that no venting should take place until the pressure inside the reactor containment vessel reached a specified threshold, with no regard to the wind directions at the time. In the case of Fukushima, an earlier venting could have dramatically reduced the impact: Much of the release happened when winds were blowing directly inland, but earlier the wind had been blowing offshore.

      “That pressure-release mechanism has not been a major focus of the engineering community,” she says. While there is appropriate attention to measures that prevent a core meltdown in the first place, “this sort of last line of defense has not been the main focus and should get more attention.”

      Wainwright says the study also underlines several successes in the management of the Fukushima accident. Many of the safety systems did work as they were designed. For example, even though the oldest reactor, unit 1, suffered the greatest internal damage, it released little radioactive material. Most people were able to evacuate from the 20-kilometer (12-mile) zone before the largest release happened. The mitigation measures were “somewhat successful,” Wainwright says. But there was tremendous confusion and anger during and after the accident because there were no preparations in place for such an event.

      Much work has focused on ways to prevent the kind of accidents that happened at Fukushima — for example, in the U.S. reactor operators can deploy portable backup power supplies to maintain proper reactor cooling at any reactor site. But the ongoing situation at the Zaporizhzhia nuclear complex in Ukraine, where nuclear safety is challenged by acts of war, demonstrates that despite engineers’ and operators’ best efforts to prevent it, “the totally unexpected could still happen,” Wainwright says.

      “The big-picture message is that we should have equal attention to both prevention and mitigation of accidents,” she says. “This is the essence of resilience, and it applies beyond nuclear power plants to all essential infrastructure of a functioning society, for example, the electric grid, the food and water supply, the transportation sector, etc.”

      One thing the researchers recommend is that in designing evacuation protocols, planners should make more effort to learn from much more frequent disasters such as wildfires and hurricanes. “We think getting more interdisciplinary, transdisciplinary knowledge from other kinds of disasters would be essential,” she says. Most of the emergency response strategies presently in place, she says, were designed in the 1980s and ’90s, and need to be modernized. “Consequences can be mitigated. A nuclear accident does not have to be a catastrophe, as is often portrayed in popular culture,” Wainright says.

      The research team included Giovanni Sansavini at ETH Zurich in Switzerland; Randall Gauntt at Sandia National Laboratories in New Mexico; and Kimiaki Saito at the Japan Atomic Energy Agency. More

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      in Environment

      New tool predicts flood risk from hurricanes in a warming climate

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      Coastal cities and communities will face more frequent major hurricanes with climate change in the coming years. To help prepare coastal cities against future storms, MIT scientists have developed a method to predict how much flooding a coastal community is likely to experience as hurricanes evolve over the next decades.

      When hurricanes make landfall, strong winds whip up salty ocean waters that generate storm surge in coastal regions. As the storms move over land, torrential rainfall can induce further flooding inland. When multiple flood sources such as storm surge and rainfall interact, they can compound a hurricane’s hazards, leading to significantly more flooding than would result from any one source alone. The new study introduces a physics-based method for predicting how the risk of such complex, compound flooding may evolve under a warming climate in coastal cities.

      One example of compound flooding’s impact is the aftermath from Hurricane Sandy in 2012. The storm made landfall on the East Coast of the United States as heavy winds whipped up a towering storm surge that combined with rainfall-driven flooding in some areas to cause historic and devastating floods across New York and New Jersey.

      In their study, the MIT team applied the new compound flood-modeling method to New York City to predict how climate change may influence the risk of compound flooding from Sandy-like hurricanes over the next decades.  

      They found that, in today’s climate, a Sandy-level compound flooding event will likely hit New York City every 150 years. By midcentury, a warmer climate will drive up the frequency of such flooding, to every 60 years. At the end of the century, destructive Sandy-like floods will deluge the city every 30 years — a fivefold increase compared to the present climate.

      “Long-term average damages from weather hazards are usually dominated by the rare, intense events like Hurricane Sandy,” says study co-author Kerry Emanuel, professor emeritus of atmospheric science at MIT. “It is important to get these right.”

      While these are sobering projections, the researchers hope the flood forecasts can help city planners prepare and protect against future disasters. “Our methodology equips coastal city authorities and policymakers with essential tools to conduct compound flooding risk assessments from hurricanes in coastal cities at a detailed, granular level, extending to each street or building, in both current and future decades,” says study author Ali Sarhadi, a postdoc in MIT’s Department of Earth, Atmospheric and Planetary Sciences.

      The team’s open-access study appears online today in the Bulletin of the American Meteorological Society. Co-authors include Raphaël Rousseau-Rizzi at MIT’s Lorenz Center, Kyle Mandli at Columbia University, Jeffrey Neal at the University of Bristol, Michael Wiper at the Charles III University of Madrid, and Monika Feldmann at the Swiss Federal Institute of Technology Lausanne.

      The seeds of floods

      To forecast a region’s flood risk, weather modelers typically look to the past. Historical records contain measurements of previous hurricanes’ wind speeds, rainfall, and spatial extent, which scientists use to predict where and how much flooding may occur with coming storms. But Sarhadi believes that the limitations and brevity of these historical records are insufficient for predicting future hurricanes’ risks.

      “Even if we had lengthy historical records, they wouldn’t be a good guide for future risks because of climate change,” he says. “Climate change is changing the structural characteristics, frequency, intensity, and movement of hurricanes, and we cannot rely on the past.”

      Sarhadi and his colleagues instead looked to predict a region’s risk of hurricane flooding in a changing climate using a physics-based risk assessment methodology. They first paired simulations of hurricane activity with coupled ocean and atmospheric models over time. With the hurricane simulations, developed originally by Emanuel, the researchers virtually scatter tens of thousands of “seeds” of hurricanes into a simulated climate. Most seeds dissipate, while a few grow into category-level storms, depending on the conditions of the ocean and atmosphere.

      When the team drives these hurricane simulations with climate models of ocean and atmospheric conditions under certain global temperature projections, they can see how hurricanes change, for instance in terms of intensity, frequency, and size, under past, current, and future climate conditions.

      The team then sought to precisely predict the level and degree of compound flooding from future hurricanes in coastal cities. The researchers first used rainfall models to simulate rain intensity for a large number of simulated hurricanes, then applied numerical models to hydraulically translate that rainfall intensity into flooding on the ground during landfalling of hurricanes, given information about a region such as its surface and topography characteristics. They also simulated the same hurricanes’ storm surges, using hydrodynamic models to translate hurricanes’ maximum wind speed and sea level pressure into surge height in coastal areas. The simulation further assessed the propagation of ocean waters into coastal areas, causing coastal flooding.

      Then, the team developed a numerical hydrodynamic model to predict how two sources of hurricane-induced flooding, such as storm surge and rain-driven flooding, would simultaneously interact through time and space, as simulated hurricanes make landfall in coastal regions such as New York City, in both current and future climates.  

      “There’s a complex, nonlinear hydrodynamic interaction between saltwater surge-driven flooding and freshwater rainfall-driven flooding, that forms compound flooding that a lot of existing methods ignore,” Sarhadi says. “As a result, they underestimate the risk of compound flooding.”

      Amplified risk

      With their flood-forecasting method in place, the team applied it to a specific test case: New York City. They used the multipronged method to predict the city’s risk of compound flooding from hurricanes, and more specifically from Sandy-like hurricanes, in present and future climates. Their simulations showed that the city’s odds of experiencing Sandy-like flooding will increase significantly over the next decades as the climate warms, from once every 150 years in the current climate, to every 60 years by 2050, and every 30 years by 2099.

      Interestingly, they found that much of this increase in risk has less to do with how hurricanes themselves will change with warming climates, but with how sea levels will increase around the world.

      “In future decades, we will experience sea level rise in coastal areas, and we also incorporated that effect into our models to see how much that would increase the risk of compound flooding,” Sarhadi explains. “And in fact, we see sea level rise is playing a major role in amplifying the risk of compound flooding from hurricanes in New York City.”

      The team’s methodology can be applied to any coastal city to assess the risk of compound flooding from hurricanes and extratropical storms. With this approach, Sarhadi hopes decision-makers can make informed decisions regarding the implementation of adaptive measures, such as reinforcing coastal defenses to enhance infrastructure and community resilience.

      “Another aspect highlighting the urgency of our research is the projected 25 percent increase in coastal populations by midcentury, leading to heightened exposure to damaging storms,” Sarhadi says. “Additionally, we have trillions of dollars in assets situated in coastal flood-prone areas, necessitating proactive strategies to reduce damages from compound flooding from hurricanes under a warming climate.”

      This research was supported, in part, by Homesite Insurance. More

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      Q&A: Are far-reaching fires the new normal?

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      Where there’s smoke, there is fire. But with climate change, larger and longer-burning wildfires are sending smoke farther from their source, often to places that are unaccustomed to the exposure. That’s been the case this week, as smoke continues to drift south from massive wildfires in Canada, prompting warnings of hazardous air quality, and poor visibility in states across New England, the mid-Atlantic, and the Midwest.

      As wildfire season is just getting going, many may be wondering: Are the air-polluting effects of wildfires a new normal?

      MIT News spoke with Professor Colette Heald of the Department of Civil and Environmental Engineering and the Department of Earth, Atmospheric and Planetary Sciences, and Professor Noelle Selin of the Institute for Data, Systems and Society and the Department of Earth, Atmospheric and Planetary Sciences. Heald specializes in atmospheric chemistry and has studied the climate and health effects associated with recent wildfires, while Selin works with atmospheric models to track air pollutants around the world, which she uses to inform policy decisions on mitigating  pollution and climate change. The researchers shared some of their insights on the immediate impacts of Canada’s current wildfires and what downwind regions may expect in the coming months, as the wildfire season stretches into summer.  

      Q: What role has climate change and human activity played in the wildfires we’ve seen so far this year?

      Heald: Unusually warm and dry conditions have dramatically increased fire susceptibility in Canada this year. Human-induced climate change makes such dry and warm conditions more likely. Smoke from fires in Alberta and Nova Scotia in May, and Quebec in early June, has led to some of the worst air quality conditions measured locally in Canada. This same smoke has been transported into the United States and degraded air quality here as well. Local officials have determined that ignitions have been associated with lightning strikes, but human activity has also played a role igniting some of the fires in Alberta.

      Q: What can we expect for the coming months in terms of the pattern of wildfires and their associated air pollution across the United States?

      Heald: The Government of Canada is projecting higher-than-normal fire activity throughout the 2023 fire season. Fire susceptibility will continue to respond to changing weather conditions, and whether the U.S. is impacted will depend on the winds and how air is transported across those regions. So far, the fire season in the United States has been below average, but fire risk is expected to increase modestly through the summer, so we may see local smoke influences as well.

      Q: How has air pollution from wildfires affected human health in the U.S. this year so far?

      Selin: The pollutant of most concern in wildfire smoke is fine particulate matter (PM2.5) – fine particles in the atmosphere that can be inhaled deep into the lungs, causing health damages. Exposure to PM2.5 causes respiratory and cardiovascular damage, including heart attacks and premature deaths. It can also cause symptoms like coughing and difficulty breathing. In New England this week, people have been breathing much higher concentrations of PM2.5 than usual. People who are particularly vulnerable to the effects are likely experiencing more severe impacts, such as older people and people with underlying conditions. But PM2.5 affects everyone. While the number and impact of wildfires varies from year to year, the associated air pollution from them generally lead to tens of thousands of premature deaths in the U.S. overall annually. There is also some evidence that PM2.5 from fires could be particularly damaging to health.

      While we in New England usually have relatively lower levels of pollution, it’s important also to note that some cities around the globe experience very high PM2.5 on a regular basis, not only from wildfires, but other sources such as power plants and industry. So, while we’re feeling the effects over the past few days, we should remember the broader importance of reducing PM2.5 levels overall for human health everywhere.

      Q: While firefighters battle fires directly this wildfire season, what can we do to reduce the effects of associated air pollution? And what can we do in the long-term, to prevent or reduce wildfire impacts?

      Selin: In the short term, protecting yourself from the impacts of PM2.5 is important. Limiting time outdoors, avoiding outdoor exercise, and wearing a high-quality mask are some strategies that can minimize exposure. Air filters can help reduce the concentrations of particles in indoor air. Taking measures to avoid exposure is particularly important for vulnerable groups. It’s also important to note that these strategies aren’t equally possible for everyone (for example, people who work outside) — stressing the importance of developing new strategies to address the underlying causes of increasing wildfires.

      Over the long term, mitigating climate change is important — because warm and dry conditions lead to wildfires, warming increases fire risk. Preventing the fires that are ignited by people or human activities can help.  Another way that damages can be mitigated in the longer term is by exploring land management strategies that could help manage fire intensity. More

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      Preparing to be prepared

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      The Kobe earthquake of 1995 devastated one of Japan’s major cities, leaving over 6,000 people dead while destroying or making unusable hundreds of thousands of structures. It toppled elevated freeway segments, wrecked mass transit systems, and damaged the city’s port capacity.

      “It was a shock to a highly engineered, urban city to have undergone that much destruction,” says Miho Mazereeuw, an associate professor at MIT who specializes in disaster resilience.

      Even in a country like Japan, with advanced engineering, and policies in place to update safety codes, natural forces can overwhelm the built environment.

      “There’s nothing that’s ever guaranteed safe,” says Mazereeuw, an associate professor of architecture and urbanism in MIT’s Department of Architecture and director of the Urban Risk Lab. “We [think that] through technology and engineering we can solve things and fight nature. Whereas it’s really that we’re living with nature. We’re part of this natural ecosystem.”

      That’s why Mazereeuw’s work on disaster resilience focuses on plans, people, and policies, well as technology and design to prepare for the future. In the Urban Risk Lab, which Mazereeuw founded, several projects are based on the design of physical objects, spaces, and software platforms, but many others involve community-level efforts, so that local governments have workable procedures in case of emergency.

      “What we can do for ourselves and each other is have plans in place so that if something does happen, the level of chaos and fear can be reduced and we can all be there to help each other through,” Mazereeuw says. When it comes to disaster preparedness, she adds, “Definitely a lot of it is on the built environment side of things, but a lot of it is also social, making sure that in our communities, we know who would need help, and we have those kinds of relationships beforehand.”

      The Kobe earthquake was a highly influential event for Mazereeuw. She has researched the response to it and has a book coming out about natural disasters, policies, and design in Japan. Beyond that, the Kobe event helped reinforce her sense that when it comes to disaster preparedness, progress can be made many ways. For her research, teaching, and innovative work at the Urban Risk Lab, Mazereeuw was granted tenure at MIT last year.

      Two cultures grappling with nature

      Mazereeuw has one Dutch parent and one Japanese parent, and both cultures helped produce her interest in managing natural forces. On her Dutch side, many family friends were involved with local government and water management — practically an existential issue in a country that sits largely below sea level.

      Mazereeuw’s parents, however, were living in Japan in 1995. And while they happened to be away while the Kobe earthquake hit, her Japanese links helped spur her interest in studying the event and its aftermath.

      “I think that was a wake-up call for me, too, about how we need to plan and design cities to reduce the impact of chaos at the time of disasters,” Mazereeuw says.

      Mazereeuw earned her undergraduate degree from Wesleyan University, majoring in earth and environmental sciences and in studio art. After working in an architectural office in Tokyo, she decided to attend graduate school, receiving her dual masters from Harvard University’s Graduate School of Design, with a thesis about Kobe and disaster readiness. She then worked in architecture offices, including the Office of Metropolitan Architecture in Rotterdam, but returned to academia to work on climate change and disaster resilience.   

      Mazereeuw’s book, “Design Before Disaster,” explores this subject in depth, from urban planning to coastal-safety strategies to community-based design frameworks, and is forthcoming from the University of Virginia Press.

      Since joining the MIT faculty, Mazereeuw has also devoted significant time to the launch and growth of the Urban Risk Lab, an interdisciplinary group working on an array of disaster-preparedness efforts. One such project has seen lab members work with local officials from many places — including Massachusetts, California, Georgia, and Puerto Rico — to add to their own disaster-preparedness planning.

      A plan developed by local officials with community input, Mazereeuw suggests, will likely function better than one produced by, say, consultants from outside a community, as she has seen happen many times: “A report on a dusty shelf isn’t actionable,” she says. “This way it’s a decision-making process by the people involved.”

      In a project based on physical design, the Urban Risk Lab has also been working with the U.S. Federal Emergency Management Agency on an effort to produce temporary postdisaster housing for the OCONUS region (Alaska, Hawaii, and other U.S. overseas territories). The lab’s design, called SEED (Shelter for Emergency Expansion Design), features a house that is compact enough to be shipped anywhere and unfolds on-site, while being sturdy enough to withstand follow-up events such as hurricanes, and durable enough to be incorporated into longer-term housing designs.

      “We felt it had to be really, really good quality, so it would be a resource, rather than something temporary that disintegrates after five years,” Mazereeuw says. “It’s built to be a small safety shelter but also could be part of a permanent house.”

      A grand challenge, and a plethora of projects

      Mazereeuw is also a co-lead of one of the five multiyear projects selected in 2022 to move forward as part of MIT’s Climate Grand Challenges competition. Along with Kerry Emanuel and Paul O’Gorman, of MIT’s Department of Earth, Atmospheric and Planetary Sciences, Mazereeuw will help direct a project advancing climate modeling by quantifying the risk of extreme weather events for specific locations. The idea is to help vulnerable urban centers and other communities prepare for such events.

      The Urban Risk Lab has many other kinds of projects in its portfolio, following Mazereeuw’s own interest in conceptualizing disaster preparedness broadly. In collaboration with officials in Japan, and with support from Google, lab members worked on interactive, real-time flood-mapping software, in which residents can help officials know where local flooding has reached emergency levels. The researchers also created an AI module to prioritize the information.

      “Residents really have the most localized information, which you can’t get from a satellite,” Mazereeuw says. “They’re also the ones who learn about it first, so they have a lot of information that emergency managers can use for their response. The program is really meant to be a conduit between the efforts of emergency managers and residents, so that information flow can go in both directions.”

      Lab members in the past have also mapped the porosity of the MIT campus, another effort that used firsthand knowledge. Additionally, lab members are currently engaging with a university in Chile to design tsunami response strategies; developing a community mapping toolkit for resilience planning in Thailand and Vietnam; and working with Mass Audubon to design interactive furniture for children to learn about ecology.  

      “Everything is tied together with this interest in raising awareness and engaging people,” Mazereeuw says.

      That also describes Mazereeuw’s attitude about participation in the Urban Risk Lab, a highly cross-disciplinary place with members who have gravitated to it from around MIT.

      “Our lab is extremely interdisciplinary,” Mazereeuw says. “We have students coming in from all over, from different parts of campus. We have computer science and engineering students coming into the lab and staying to get their graduate degrees alongside many architecture and planning students.” The lab also has five full-time researchers — Aditya Barve, Larisa Ovalles, Mayank Ojha, Eakapob Huangthananpan, and Saeko Baird — who lead their own projects and research groups.

      What those lab members have in common is a willingness to think proactively about reducing disaster impacts. Being prepared for those events itself requires preparation.

      Even in the design world, Mazereeuw says, “People are reactive. Because something has happened, that’s when they go in to help. But I think we can have a larger impact by anticipating and designing for these issues beforehand.” More

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      New nanosatellite tests autonomy in space

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      In May 2022, a SpaceX Falcon 9 rocket launched the Transporter-5 mission into orbit. The mission contained a collection of micro and nanosatellites from both industry and government, including one from MIT Lincoln Laboratory called the Agile MicroSat (AMS).

      AMS’s primary mission is to test automated maneuvering capabilities in the tumultuous very low-Earth orbit (VLEO) environment, starting at 525 kilometers above the surface and lowering down. VLEO is a challenging location for satellites because the higher air density, coupled with variable space weather, causes increased and unpredictable drag that requires frequent maneuvers to maintain position. Using a commercial off-the-shelf electric-ion propulsion system and custom algorithms, AMS is testing how well it can execute automated navigation and control over an initial mission period of six months.

      “AMS integrates electric propulsion and autonomous navigation and guidance control algorithms that push a lot of the operation of the thruster onto the spacecraft — somewhat like a self-driving car,” says Andrew Stimac, who is the principal investigator for the AMS program and the leader of the laboratory’s Integrated Systems and Concepts Group.

      Stimac sees AMS as a kind of pathfinder mission for the field of small satellite autonomy. Autonomy is essential to support the growing number of small satellite launches for industry and science because it can reduce the cost and labor needed to maintain them, enable missions that call for quick and impromptu responses, and help to avoid collisions in an already-crowded sky.

      AMS is the first-ever test of a nanosatellite with this type of automated maneuvering capability.

      AMS uses an electric propulsion thruster that was selected to meet the size and power constraints of a nanosatellite while providing enough thrust and endurance to enable multiyear missions that operate in VLEO. The flight software, called the Bus Hosted Onboard Software Suite, was designed to autonomously operate the thruster to change the spacecraft’s orbit. Operators on the ground can give AMS a high-level command, such as to descend to and maintain a 300-kilometer orbit, and the software will schedule thruster burns to achieve that command autonomously, using measurements from the onboard GPS receiver as feedback. This experimental software is separate from the bus flight software, which allows AMS to safely test its novel algorithms without endangering the spacecraft.

      “One of the enablers for AMS is the way in which we’ve created this software sandbox onboard the spacecraft,” says Robert Legge, who is another member of the AMS team. “We have our own hosted software that’s running on the primary flight computer, but it’s separate from the critical health and safety avionics software. Basically, you can view this as being a little development environment on the spacecraft where we can test out different algorithms.”

      AMS has two secondary missions called Camera and Beacon. Camera’s mission is to take photos and short video clips of the Earth’s surface while AMS is in different low-Earth orbit positions.

      “One of the things we’re hoping to demonstrate is the ability to respond to current events,” says Rebecca Keenan, who helped to prepare the Camera payload. “We could hear about something that happened, like a fire or flood, and then respond pretty quickly to maneuver the satellite to image it.”

      Keenan and the rest of the AMS team are collaborating with the laboratory’s DisasterSat program, which aims to improve satellite image processing pipelines to help relief agencies respond to disasters more quickly. Small satellites that could schedule operations on-demand, rather than planning them months in advance before launch, could be a great asset to disaster response efforts.

      The other payload, Beacon, is testing new adaptive optics capabilities for tracking fast-moving targets by sending laser light from the moving satellite to a ground station at the laboratory’s Haystack Observatory in Westford, Massachusetts. Enabling precise laser pointing from an agile satellite could aid many different types of space missions, such as communications and tracking space debris. It could also be used for emerging programs such as Breakthrough Starshot, which is developing a satellite that can accelerate to high speeds using a laser-propelled lightsail.

      “As far as we know, this is the first on-orbit artificial guide star that has launched for a dedicated adaptive optics purpose,” says Lulu Liu, who worked on the Beacon payload. “Theoretically, the laser it carries can be maneuvered into position on other spacecraft to support a large number of science missions in different regions of the sky.”

      The team developed Beacon with a strict budget and timeline and hope that its success will shorten the design and test loop of next-generation laser transmitter systems. “The idea is that we could have a number of these flying in the sky at once, and a ground system can point to one of them and get near-real-time feedback on its performance,” says Liu.

      AMS weighs under 12 kilograms with 6U dimensions (23 x 11 x 36 centimeters). The bus was designed by Blue Canyon Technologies and the thruster was designed by Enpulsion GmbH.

      Legge says that the AMS program was approached as an opportunity for Lincoln Laboratory to showcase its ability to conduct work in the space domain quickly and flexibly. Some major roadblocks to rapid development of new space technology have been long timelines, high costs, and the extremely low risk tolerance associated with traditional space programs. “We wanted to show that we can really do rapid prototyping and testing of space hardware and software on orbit at an affordable cost,” Legge says.

      “AMS shows the value and fast time-to-orbit afforded by teaming with rapid space commercial partners for spacecraft core bus technologies and launch and ground segment operations, while allowing the laboratory to focus on innovative mission concepts, advanced components and payloads, and algorithms and processing software,” says Dan Cousins, who is the program manager for AMS. “The AMS team appreciates the support from the laboratory’s Technology Office for allowing us to showcase an effective operating model for rapid space programs.”

      AMS took its first image on June 1, completed its thruster commissioning in July, and has begun to descend toward its target VLEO position. More

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      Migration Summit addresses education and workforce development in displacement

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      “Refugees can change the world with access to education,” says Alnarjes Harba, a refugee from Syria who recently shared her story at the 2022 Migration Summit — a first-of-its-kind, global convening to address the challenges that displaced communities face in accessing education and employment.

      At the age of 13, Harba was displaced to Lebanon, where she graduated at the top of her high school class. But because of her refugee status, she recalls, no university in her host country would accept her. Today, Harba is a researcher in health-care architecture. She holds a bachelor’s degree from Southern New Hampshire University, where she was part of the Global Education Movement, a program providing refugees with pathways to higher education and work.

      Like many of the Migration Summit’s participants, Harba shared her story to call attention not only to the barriers to refugee education, but also to the opportunities to create more education-to-employment pathways like MIT Refugee Action Hub’s (ReACT) certificate programs for displaced learners.

      Organized by MIT ReACT, the MIT Abdul Latif Jameel World Education Lab (J-WEL), Na’amal, Karam Foundation, and Paper Airplanes, the Migration Summit sought to center the voices and experiences of those most directly impacted by displacement — both in narratives about the crisis and in the search for solutions. Themed “Education and Workforce Development in Displacement,” this year’s summit welcomed more than 900 attendees from over 30 countries, to a total of 40 interactive virtual sessions led by displaced learners, educators, and activists working to support communities in displacement.

      Sessions highlighted the experiences of refugees, migrants, and displaced learners, as well as current efforts across the education and workforce development landscape, ranging from pK-12 initiatives to post-secondary programs, workforce training to entrepreneurship opportunities.

      Overcoming barriers to access

      The vision for the Migration Summit developed, in part, out of the need to raise more awareness about the long-standing global displacement crisis. According to the United Nations High Commissioner for Refugees (UNHCR), 82.4 million people worldwide today are forcibly displaced, a figure that doesn’t include the estimated 12 million people who have fled their homes in Ukraine since February.

      “Refugees not only leave their countries; they leave behind a thousand memories, their friends, their families,” says Mondiant Dogon, a human rights activist, refugee ambassador, and author who gave the Migration Summit’s opening keynote address. “Education is the most important thing that can happen to refugees. In that way, we can leave behind the refugee camps and build our own independent future.”

      Yet, as the stories of the summit’s participants highlight, many in displacement have lost their livelihoods or had their education disrupted — only to face further challenges when trying to access education or find work in their new places of residence. Obstacles range from legal restrictions, language and cultural barriers, and unaffordable costs to lack of verifiable credentials. UNHCR estimates that only 5 percent of refugees have access to higher education, compared to the global average of 39 percent.

      “There is another problem related to forced displacement — dehumanization of migrants,” says Lina Sergie Attar, the founder and CEO of Karam Foundation. “They are unjustly positioned as enemies, as a threat.”

      But as Blein Alem, an MIT ReACT alum and refugee from Eritrea, explains, “No one chooses to be a refugee — it just occurs. Whether by conflict, war, human rights violations, just because you have refugee status does not mean that you are not willing to make a change in your life and access to education and work.” Several participants, including Alem, shared that, even with a degree in hand, their refugee status limited their ability to work in their new countries of residence.

      Displaced communities face complex and structural challenges in accessing education and workforce development opportunities. Because of the varying and vast effects of displacement, efforts to address these challenges range in scale and focus and differ across sectors. As Lorraine Charles, co-founder and director of Na’amal, noted in the Migration Summit’s closing session, many organizations find themselves working in silos, or even competing with each other for funding and other resources. As a result, solution-making has been fragmented, with persistent gaps between different sectors that are, in fact, working toward the same goals.

      Imagining a modular, digital, collaborative approach

      A key takeaway from the month’s discussions, then, is the need to rethink the response to refugee education and workforce challenges. During the session, “From Intentions to Impact: Decolonizing Refugee Response,” participants emphasized the systemic nature of these challenges. Yet formal responses, such as the 1951 Refugee Convention, have been largely inadequate — in some instances even oppressing the communities they’re meant to support, explains Sana Mustafa, director of partnership and engagement for Asylum Access.

      “We have the opportunity to rethink how we are handling the situation,” Mustafa says, calling for more efforts to include refugees in the design and development of solutions.

      Presenters also agreed that educational institutions, particularly universities, could play a vital role in providing more pathways for refugees and displaced learners. Key to this is rethinking the structure of education itself, including its delivery.

      “The challenge right now is that degrees are monolithic,” says Sanjay Sarma, vice president for MIT Open Learning, who gave the keynote address on “Pathways to Education, Livelihood, and Hope.” “They’re like those gigantic rocks at Stonehenge or in other megalithic sites. What we need is a much more granular version of education: bricks. Bricks were invented several thousand years ago, but we don’t really have that yet formally and extensively in education.”

      “There is no way we can accommodate thousands and thousands of refugees face-to-face,” says Shai Reshef, the founder and president of University of the People. “The only path is a digital one.”

      Ultimately, explains Demetri Fadel of Karam Foundation, “We really need to think about how to create a vision of education as a right for every person all around the world.”

      Underlying many of the Migration Summit’s conclusions is the awareness that there is still much work to be done. However, as the summit’s co-chair Lana Cook said in her closing remarks, “This was not a convening of despair, but one about what we can build together.”

      The summit’s organizers are currently putting together a public report of the key findings that have emerged from the month’s conversations, including recommendations for thematic working groups and future Migration Summit activities. More