Powell Research Lab

Prof. Brian Powell is a Physical Oceanographer in the Department of Oceanography at the University of Hawai‘i at Mānoa and a Senior Fellow at the Joint Institute for Marine and Atmospheric Research. His research focuses on the understanding and prediction of the ocean environment, including physics and biogeochemistry, applications of machine learning in state estimation and prediction, and ocean observations and their design. He was the recipient of the 2009 ONR Young Investigator award for his work on prediction and state estimation. His lab welcomes people from any background and discipline interested in the ocean and its climate. Ideas within the lab group are shared and debated openly with all discourse free of rigid hierarchies.

Physics and Ecosystems

Work in the Powell lab is aimed to understand the physics and feedbacks that control the ocean, its geochemical cycle, and the ecosystems that form in each niche. These physical processes determine the health of everything from coral reefs to nearshore and pelagic fish stocks that feed humans.

Over the past thirty years, the variability of extreme events has likely increased (prolonged marine heat waves, potentially stronger El Niño impacts, etc.); however, the climate itself has not yet significantly shifted. Two crucial questions related to climate change are: (i) how will interannual–to–decadal variability change over the coming decades; (ii) and, when will climate change emerge as the dominant signal?

As the variability in the physics increase towards a new climate change signal, the impacts on the biogeochemistry and ecosystems may be profound. These changes will be felt from the single–cell planktons to the dinner–tables of families and reach from science to economics to social convention.

Simulations and Machine Learning

To understand and forecast the ocean and its ecosystems, we rely upon large–scale, high–performance computing. We develop software tools for simulating the ocean and its environment, for data analysis, and for combining observations and models in mathematically consistent ways so that the models represent the observations of the ocean.

Machine Learning refers to a broad range of methods that constrain a model with observed behavior. From simple linear–regression to deep neural–networks, there exist many ways to combine models and data. Less than 1% of the ocean is observed, which means that traditional, statistics–based machine learning is an inappropriate tool: a naïve neural-network lacks the observations to "learn" about the ocean. Physics-informed methods, such as 4D-Variational Data Assimilation, Ensemble Kalman Filters, and Physics-Informed Neural Networks (PINN) are the methods that are required.

Our lab group strives to continue to be at the forefront of these techniques and utilizing them to blur any boundaries between models and observations. We look to quantify what observations actually "see" in the ocean, which observations improve our forecasting and understanding, and how machine–learning can supplement or replace our estimations of small–scale processes in the ocean.


💻 powellb hawaii edu
🤝 Marine Sciences Building #226
🏛 University of Hawai‘i at Mānoa
1000 Pope Road, MSB
Honolulu, Hawaii 96822