AI Research
Can extreme weather be predicted? UWL researcher uses artificial intelligence to help protect vulnerable communities from the world’s most dangerous storms
Rupsa Bhowmick uses artificial intelligence—specifically machine learning techniques such as decision trees, random forests, and XGBoost with explainable AI (XAI) approaches—to improve classification and prediction of rapidly intensifying cyclones. Her models analyze environmental factors such as ocean temperature, wind patterns, and humidity to improve early warning systems.
When extreme weather strikes, it can change lives in an instant. That’s why Rupsa Bhowmick, assistant professor of Geography and Environmental Science at UW-La Crosse, is using artificial intelligence (AI) to make forecasting faster, smarter, and more accurate — especially for communities most at risk.
Bhowmick’s research focuses on predicting tropical cyclones in the Southwest Pacific, a region where island nations like Fiji, Vanuatu, and New Caledonia are increasingly vulnerable to destructive storms fueled by warming oceans. She’s also applying these methods to the U.S. Midwest, where extreme weather like floods, blizzards and tornadoes pose growing threats.
Whether across the globe or close to home, Bhowmick’s mission is clear: improve forecasts to save lives.
“Extreme weather events can turn our lives upside down in seconds,” Bhowmick says. “Through research, we can improve forecasting, better communicate risk, and design infrastructure that’s ready for what’s coming.”
A personal drive: From India’s Bay of Bengal to global forecasting
Bhowmick’s passion for weather research began with personal experience. Growing up near the Bay of Bengal in India, she witnessed firsthand the devastating impact of cyclones and flooding.
In her community, outcomes often depend on economic status. Families with means could recover quickly. Those living in low-income or slum districts faced far greater challenges—including displacement and permanent loss of homes.
“Even as a child, I wondered why the impact of the same storm could vary so much from one region to the next,” she recalls. “That’s what led me to study geography and weather—to find answers that could help people.”
As a graduate student, Bhowmick turned her attention to the Southwest Pacific, a cyclone-prone region where many communities lack the resources to recover after disasters. Her work focuses on developing machine learning methods to classify and predict cyclone intensity evolution—especially before landfall—by integrating supervised learning with geospatial diagnostics. This work resulted in a scalable and interpretable framework for probabilistic intensity forecasting, aimed at supporting climate-resilient hazard planning in vulnerable regions.
She also studies extreme cyclone risk estimation, helping map where extreme cyclones – Category 4 and beyond – are likely to strike, carrying catastrophic impacts. Additionally, she studies how warmer ocean temperatures are making these extreme storms stronger and more frequent.
AI for cyclone forecasting
A major breakthrough in Bhowmick’s research is her use of machine learning—a subset of AI—to improve cyclone prediction, especially for rapidly intensifying (RI) cyclones.
These storms strengthen dramatically in a short time, often catching communities off guard. Traditional weather models struggle to predict RI events because they involve complex interactions between multiple environmental factors.
Bhowmick’s machine learning models—such as decision trees, random forests, and XGBoost—analyze massive datasets of cyclone behavior alongside environmental conditions like ocean heat, humidity, and wind patterns. The goal: spot patterns and correctly identify RI events earlier, giving people more time to prepare.
“Machine learning can process many interacting variables at once, helping to avoid issues like multicollinearity while uncovering complex, non-linear patterns in the data” she explains. “That’s what makes it so powerful for predicting rapid intensification, which is still one of the biggest challenges in weather forecasting.”
Bringing research home: extreme weather in the Midwest
While her early work focused on tropical regions, Bhowmick is now applying her expertise to the Midwestern U.S., where communities face a different kind of storm: extra-tropical cyclones.
These storms do not form over warm ocean waters like tropical cyclones, but rather from the interaction of contrasting air masses along the jet stream, particularly between October and March. They can bring intense winds, heavy snow, thunderstorms, and even dangerous waves on the Great Lakes. Some evolve into ‘bomb cyclones,’ rapidly intensifying within 24 hours and producing intense winds and blizzard conditions.
Bhowmick is using machine learning to study these storms’ behavior, including their intensity, speed, frequency, and how long-term oceanic-atmospheric trends may influence them.
“Whether it’s a tropical or extra-tropical cyclone, the impact can be devastating if people aren’t warned in time,” she says. “My research aims to give communities the tools they need to stay safe.”
Teaching the next generation
In addition to her research, Bhowmick teaches physical geography and climatology courses at UWL and is actively working to involve students in her projects. She sees education as a critical part of the solution—training the next generation to use data and technology to improve public safety.
“Now my training inspires me to translate this knowledge into action,” she says. “I want students to learn how to build these models, analyze extreme weather, and apply their skills to help communities prepare for the worst.”
So — can extreme weather be predicted?
The answer is yes, though it’s not easy.
Predicting extreme weather remains one of science’s toughest challenges. But thanks to advances in high-resolution data, machine learning, and deep learning-based forecasting and prediction techniques, researchers like Bhowmick are making real progress.
“This research is all about people,” she says. “With better forecasts and more resilient infrastructure, we can reduce loss—of property and, more importantly, of life.”
Written by UW-La Crosse
Link to original story: https://www.uwlax.edu/news/posts/can-extreme-weather-be-predicted/
If you want exposure to the AI boom without the hassle of picking individual stocks, these three AI-focused ETFs offer diversified, long-term opportunities.
Artificial intelligence (AI) has been a huge catalyst for the portfolios of many investors over the past several years. Large tech companies are spending hundreds of billions of dollars to build out their AI hardware infrastructure, creating massive winners like semiconductor designer Nvidia.
But not everyone wants to go hunting for the next big AI winner, nor is it easy to know which company will stay in the lead even if you do your own research and find a great artificial intelligence stock to buy. That’s where exchange-traded funds (ETFs) can help.
If you’re afraid of missing out on the AI boom, and have around $100 to invest right now, here are three great AI exchange-traded funds that will allow you to track some of the biggest names in artificial intelligence, no matter who’s leading the pack.
Image source: Getty Images.
1. Global X Artificial Intelligence and Technology ETF
The Global X Artificial Intelligence and Technology ETF (AIQ 0.87%) is one of the top AI ETF options for investors because it holds a diverse group of around 90 stocks, spanning semiconductors, data infrastructure, and software. Its portfolio includes household names like Nvidia, Microsoft, and Alphabet, alongside lesser-known players that give investors exposure to AI companies they might not otherwise consider.
Another strength of AIQ is its global reach: the fund invests in both U.S. and international companies, providing broader diversification across the AI landscape. Of course, this targeted approach comes at a cost. AIQ’s expense ratio of 0.68% is slightly higher than the average ETF (around 0.56%), but it’s in line with other AI-focused funds.
Performance-wise, the Global X Artificial Intelligence and Technology ETF has rewarded investors. Over the past three years, it gained 117%, trouncing the S&P 500‘s 63% return over the same period. While past performance doesn’t guarantee future results, this track record shows how powerful exposure to AI-focused companies can be.
2. Global X Robotics and Artificial Intelligence ETF
As its name suggests, the Global X Robotics and Artificial Intelligence ETF (BOTZ -0.21%) focuses on both robotics and artificial intelligence companies, as well as automation investments. Two key holdings in the fund are Pegasystems, which is an automation software company, as well as Intuitive Surgical, which creates robotic-assisted surgical systems. And yes, you’ll still have exposure to top AI stocks, including Nvidia as well.
Having some exposure to robotics and automation could be a wise long-term investment strategy. For example, UBS estimates that there will be 2 million humanoid robots in the workforce within the next decade and could reach 300 million by 2050 — reaching an estimated market size of $1.7 trillion.
If you’re inclined to believe that robotics is the future, the Global X Robotics and Artificial Intelligence ETF is a good way to spread out your investments across 49 individual companies that are betting on this future. You’ll pay an annual expense ratio of 0.68% for the fund, which is comparable to the Global X Artificial Intelligence and Technology ETF’s fees.
The fund has performed slightly better than the broader market over the past three years — gaining about 68%. Still, as robotics grows in the coming years, this ETF could be a good place to have some money invested.
3. iShares Future AI and Tech ETF
And finally, the iShares Future AI and Tech ETF (ARTY 1.72%) offers investors exposure to 48 global companies betting on AI infrastructure, cloud computing, and machine learning.
Some of the fund’s key holdings include the semiconductor company Advanced Micro Devices, Arista Networks, and the AI chip leader Broadcom, which just inked a $10 billion semiconductor deal with a large new client (widely believed to be OpenAI). In addition to its diversification across AI and tech companies, the iShares Future AI and Tech ETF also has a lower expense ratio than some of its peers, charging just 0.47% annually.
The fund has slightly underperformed the S&P 500 lately, gaining about 61% compared to the broader market’s 63% gains over the past three years. But with its strong diversification among tech and AI leaders, as well as its lower expense ratio, investors looking for a solid play on the future of artificial intelligence will find what they’re looking for in this ETF.
Chris Neiger has no position in any of the stocks mentioned. The Motley Fool has positions in and recommends Advanced Micro Devices, Alphabet, Arista Networks, Intuitive Surgical, Microsoft, and Nvidia. The Motley Fool recommends Broadcom and recommends the following options: long January 2026 $395 calls on Microsoft and short January 2026 $405 calls on Microsoft. The Motley Fool has a disclosure policy.
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