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/
State and local governments are experimenting with artificial intelligence but lack systematic approaches to scale these efforts effectively and integrate AI into government operations. Instead, efforts have been piecemeal and slow, leaving many practitioners struggling to keep up with the ever-evolving uses of AI for transforming governance and policy implementation.
While some state and local governments are leading in implementing the technology, AI adoption remains fragmented. Last year, some 150 state bills were considered relating to the government use of AI, governors in 10 states issued executive orders supporting the study of AI for use in government operations, and 10 legislatures tasked agencies with capturing comprehensive inventories.
Taking advantage of the opportunity presented by AI is critical as decision-makers face an increasing slate of challenging implementation problems and as technology quickly evolves and develops new capabilities. The use of AI is not without risks. Developing and adapting the necessary checks and guidance is critical but can be challenging for such dynamic technologies. Shifting from seeing AI as merely a technical capability to considering what AI technology should be asked to do can help state and local governments think more creatively and strategically. Here are some of the benefits governments are already exploring:
Administrative efficiency: Half of all states are using AI chatbots to reduce administrative burden and free staff for substantive and creative work. The Indiana General Assembly uses chatbots to answer questions about regulations and statutes. Austin, Texas, streamlines residential construction permitting with AI, while Vermont’s transportation agency inventories road signs and assesses pavement quality.
Research synthesis: AI tools help policymakers quickly access evolving best practices and evidence-based approaches. Overton’s AI platform, for example, allows policymakers to identify how existing evidence aligns with priority areas, compare policy approaches across states and nations, and match with relevant researchers and projects.
Implementation monitoring: AI fills critical gaps in program evaluation without major new investments. California’s transportation department analyzes traffic patterns to optimize highway safety and inform infrastructure investments.
Predictive modeling: AI-enabled models help test assumptions about which interventions will succeed. These models use features such as organizational characteristics, physical and contextual factors, and historical implementation data to predict success of policy interventions, and their outputs can help tailor interventions and improve outcomes and success. Applications include targeting health interventions to patients with modifiable risk factors, identifying lead service lines in municipal water systems, predicting flood response needs and flagging households at eviction risk.
Scaling up to wider adoption in policy and practice requires proactive steps by state and local governments and attendant guidance, monitoring and evaluation:
Adaptive policy framework: AI adoption often outpaces planning, and the definition of AI is often specific to its application. States need to define AI applications by sector (health, transportation, etc.) and develop adaptive operating strategies to guide and assess its impact. Thirty states have some guidance, but comprehensive approaches require clear definitions and inventories of current use.
Funding strategies: Policymakers must identify and leverage funding streams to cover the costs of procurement and training. Federal grants like the State and Local Cybersecurity Grant Program offer potential, though current authorization expires this Sept. 30. Massachusetts’ FutureTech Act exemplifies direct state investment, authorizing $1.23 billion for IT capital projects including AI.
Smart procurement: Effective AI procurement requires partnerships with vendors and suppliers and between chief information officers and procurement specialists. Contracts must ensure ethical use, performance monitoring and continuous improvement, but few states have procurement language related to AI. Speed matters — AI purchases risk obsolescence during lengthy procurement cycles.
Training and workforce development: Both current and future state and local government workforces need AI skills. Solutions include AI training academies and literacy programs for government workers, joint training programs between professional associations, and the General Services Administration’s AI Community of Practice‘s events and training. The Partnership for Public Service has recently opened up its AI Government Leadership program to state and local policymakers. Universities including Stanford and Michigan offer specialized programs for policymakers. Graduate programs in public policy, administration and law should incorporate AI governance tracks.
State AI policy development involves governor’s offices, chief information offices, security offices and legislatures. But success requires moving beyond pilot projects to systematic implementation. Governments that embrace this transition will be best positioned for future challenges. The opportunity exists now to set standards for AI-enabled governance, but it requires proactive steps in policy development, funding, procurement, workforce development and safeguards.
Joie Acosta is a senior behavioral scientist and the Global Scholar in Translation at RAND, a nonprofit, nonpartisan research institute. Sara Hughes is a senior policy researcher and the Global Scholar of Implementation at RAND and a professor of policy analysis at the RAND School of Public Policy.
Governing’s opinion columns reflect the views of their authors and not necessarily those of Governing’s editors or management.
SINGAPORE – An artificial intelligence (AI)-powered search engine is expected to accelerate legal research and free up time for more than three quarters of all lawyers working in Singapore who subscribe to legal research platform LawNet.
Developed in collaboration with the Singapore Academy of Law, this new tool allows lawyers to ask legal research questions in natural language and receive contextual, relevant responses.
It is trained on Singapore’s legal context and supported by data such as judgments, Singapore Law Reports, legislation and books.
GPT-Legal Q&A, which has been rolled out on LawNet, was launched by Justice Kwek Mean Luck on the second day of the TechLaw.Fest on Sept 11 at the Sands Expo and Convention Centre.
The earlier GPT-Legal model launched in 2024 provided summaries of unreported court judgments, and has since been used to generate more than 15,000 of them.
“This is a game-changing feature. This new function enables lawyers to ask legal research questions in natural language, and receive contextual, relevant responses, which are generated by AI grounded in LawNet’s content,” said Justice Kwek.
“It is designed to complement traditional keyword-based search by offering a more intuitive and responsive research experience.”
For a start, the feature is focused on delivering insights on contract law, as it is a fundamental area of law that underpins many specialised fields.
“This is a significant undertaking. It involves extensive development and rigorous testing, to align technology to the demands of your work. As such, we will be rolling out this implementation in phases,” said Justice Kwek.
The model will be improved to give insights into other significant areas of law like family law and criminal law.
The Infocomm Media Development Authority has also developed an agentic AI demonstrator for the Singapore Academy of Law to help corporate secretaries arrange annual general meetings (AGMs).
Agentic AI can help to perform tasks without the need for human intervention.
The AI agent can automate tasks like looking through the schedules of directors to find a time slot for AGMs.
With the AI agent offering routine corporate secretarial duties autonomously, professionals will be freed up to focus on higher-value advisory and strategic tasks.
Source: The Straits Times © SPH Media Limited. Permission required for reproduction
Hyderabad: The Institute of Public Enterprise (IPE), Hyderabad, has launched a pioneering 10-day Research Methodology Course (RMC) focused on the application of Artificial Intelligence (AI) tools in social science research. Sponsored by the Indian Council of Social Science Research (ICSSR), Ministry of Education, Government of India, the program commenced on October 6 and will run through October 16, 2025, at the IPE campus in Osmania University.
Designed exclusively for M.Phil., Ph.D., and Post-Doctoral researchers across social science disciplines, the course aims to equip young scholars with cutting-edge AI and Machine Learning (ML) skills to enhance research quality, ethical compliance, and interdisciplinary collaboration. The initiative is part of ICSSR’s Training and Capacity Building (TCB) programme and is offered free of cost, with travel and daily allowances reimbursed as per eligibility.
The course is being organized by IPE’s Centre for Data Science and Artificial Intelligence (CDSAI), under the academic leadership of Prof. S Sreenivasa Murthy, Director of IPE and Vice-Chairman of AIMS Telangana Chapter. Dr. Shaheen, Associate Professor of Information Technology & Analytics, serves as the Course Director, while Dr. Sagyan Sagarika Mohanty, Assistant Professor of Marketing, is the Co-Director.
Participants will undergo hands-on training in Python, R, Tableau, and Power BI, alongside modules on Natural Language Processing (NLP), supervised and unsupervised learning, and ethical frameworks such as the Digital Personal Data Protection (DPDP) Act, 2023.
The curriculum also includes field visits to policy labs like T-Hub and NIRDPR, mentorship for research proposal refinement, and guidance on publishing in Scopus and ABDC-indexed journals.
Speaking about the program, Dr. Shaheen emphasized the need for social scientists to evolve beyond traditional methods and embrace computational tools for data-driven insights.
“This course bridges the gap between conventional research and emerging technologies, empowering scholars to produce impactful, ethical, and future-ready research,” she said.
Seats for the course are allocated on a first-come, first-served basis. The last date for nominations is September 15, 2025. With its unique blend of technical training, ethical grounding, and publication support, the RMC at IPE intends to take a significant step to empower scholars in the process of modernizing social science research in India.
Interested candidates can contact: Dr Shaheen, Programme Director, at [email protected] or on mobile number 9866666620.
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