Hurricane forecasting has long challenged meteorologists due to the complex interplay of atmospheric and oceanic factors that influence storm intensity and trajectory. One critical yet underexplored component affecting tropical storms is the presence of tiny sea spray droplets generated from the ocean surface. These droplets, often created through the breakup of breaking waves and whitecaps, have significant impacts on storm dynamics by affecting air-sea heat and momentum exchanges. However, accurately quantifying their concentration, size distribution, and movement under the extreme wind speeds typical of hurricanes has remained an elusive challenge due to the difficulty of direct measurements in such harsh environments.
At The University of Texas at Dallas, a multidisciplinary team led by Dr. Kianoosh Yousefi, assistant professor of mechanical engineering, is pioneering a novel approach that harnesses state-of-the-art machine learning techniques combined with sophisticated laboratory experiments and high-resolution simulations to better understand sea spray dynamics. By focusing specifically on spume—foam droplets that form when breaking waves cause tiny droplets to be ejected from the ocean surface—this research aims to accurately capture the behavior of the smallest spray particles, some measuring as little as 20 micrometers in diameter. These fine droplets, roughly the width of a human hair, are critically important because of their ability to remain suspended longer in the atmosphere and influence momentum transfer between the ocean and the atmosphere during tropical storms.
The difficulty in traditional experimental methods lies in the inability to capture detailed measurements of these droplets under the extreme conditions present during hurricanes, where wind speeds can exceed 150 miles per hour. To overcome these obstacles, Dr. Yousefi’s team has developed a cutting-edge wind-wave research tunnel featuring a 40-foot-long water tank capable of generating controlled breaking waves. This unique facility allows researchers to replicate the turbulent conditions of stormy seas within a controlled environment, enabling precise measurement of spray droplet size, velocity, and concentration using advanced optical methods such as high-speed shadowgraph imaging. This technique employs high-speed cameras to track the motion and morphology of droplets with exceptional temporal and spatial resolution.
Central to the project is the creation of a machine learning model that integrates the complex physics of spray generation and transport processes. The model incorporates the spray generation function, a mathematical representation that quantifies the rate at which droplets form in response to wave breaking and wind stress. By coupling this function with parameters such as wave profile, wave slope, and wind velocity, the model aims to improve the agility and accuracy of hurricane forecasting systems substantially. Unlike traditional models reliant on sparse or indirect data, this approach leverages empirical data collected in the laboratory alongside fluid mechanics simulations, facilitating a more comprehensive predictive framework that accounts for the dynamics of sea spray under varying atmospheric conditions.
The implications of this work extend beyond academic curiosity. Improved representation of sea spray in hurricane models can lead to more accurate predictions of storm intensity and evolution, thereby enhancing preparedness and mitigation strategies for populations in coastal regions. Dr. Edward White, professor and head of the mechanical engineering department at UTD, emphasizes that this innovative research could revolutionize weather prediction: “Dr. Yousefi’s YIP award will enable him to make important advances in understanding sea spray dynamics and could meaningfully improve weather forecasting models in densely populated coastal regions.” He highlights the experimental complexities involved and underscores the combination of laboratory work with high-fidelity numerical simulations as a hallmark of this initiative.
This research project is backed by the prestigious Office of Naval Research Young Investigator Program (YIP) award, which recognizes promising early-career scientists. The YIP award provides funding of up to $742,345 over three years, enabling Dr. Yousefi and his team to push the boundaries of research in turbulent air-sea interactions, a field that sits at the nexus of fluid mechanics, oceanography, and atmospheric sciences. Yousefi’s work builds upon previous efforts supported by the National Science Foundation, including a collaborative initiative with Columbia University that explored broader aspects of air-sea interactions. Together, this body of work aims to fill significant gaps in our understanding of how microscopic physical processes at the ocean surface cascade to influence large-scale climatic phenomena.
The Flow Dynamics and Turbulence Laboratory at UTD, under Dr. Yousefi’s leadership, specializes in studying the intricate mechanics of turbulent air-sea exchanges. These phenomena include surface wave formation and breaking, turbulent bubble generation, airflow separation, and droplet entrainment—all conditions that impact the momentum and energy fluxes critical for weather system development. The newly developed wind-wave tunnel, combined with machine learning algorithms, provides an unprecedented toolset to simulate and analyze the interplay between turbulent ocean surfaces and the overlying atmosphere with unparalleled detail.
An essential insight gained from this research is the complex behavior of spume droplets, which are generated at the very interface between wind-driven waves and the atmosphere. The droplets’ transport mechanisms are heavily influenced by wind speed, wave slope, and surface roughness, among other factors. Through controlled experiments and real-time imaging, the research team aims to better characterize these dependencies, enabling the development of predictive models that can be directly coupled with operational hurricane forecasting tools.
Moreover, the integration of the spray generation function into the machine learning framework marks a significant innovation, as it encapsulates multiscale physical processes from the molecular to the mesoscale. Such an approach can dynamically adjust predictions as environmental conditions evolve, unlike static empirical formulations. This adaptability is crucial for forecasting rapidly intensifying storms where minute changes in sea spray flux can alter storm dynamics in critical ways, potentially improving early warning systems and saving lives.
Looking forward, the insights gleaned from this research hold promise not only for hurricane modeling but also for the broader field of climatology and Earth systems science. Sea spray plays an essential role in air-sea gas exchanges and aerosol formation, processes that impact global climate regulation and atmospheric chemistry. By deepening our understanding of these microphysical interaction processes, Dr. Yousefi’s work paves the way for more integrated and holistic climate models.
In the face of escalating climate change and increasingly frequent and intense tropical storms, the development of high-fidelity predictive tools is more urgent than ever. This project exemplifies how the synergy of experimental ingenuity, fluid mechanics expertise, and machine learning technology can unravel the complexities of natural phenomena once deemed too challenging to quantify. As the 2025 Office of Naval Research Young Investigator Program awardee, Dr. Yousefi stands at the forefront of these transformative advances in hurricane science, promising a new era in forecasting accuracy and resilience for vulnerable coastal communities.
Subject of Research: Sea spray dynamics and their impact on hurricane intensity prediction through machine learning and experimental fluid mechanics.
Article Title: Advancing Hurricane Forecasting: Machine Learning and Laboratory Innovations Illuminate Sea Spray Dynamics
News Publication Date: Not specified
Web References:
- https://me.utdallas.edu/people/faculty/kianoosh-yousefi/
- https://www.onr.navy.mil/2025-young-investigators
- https://labs.utdallas.edu/fdt-lab/
- https://news.utdallas.edu/science-technology/waves-wind-energy-nsf-grant-2024/
Image Credits: The University of Texas at Dallas
Keywords: Weather forecasting, Weather simulations, Earth systems science, Climatology, Atmospheric science, Air-sea interactions, Ocean waves, Wind tunnels
