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Jim Thomson

Senior Principal Oceanographer

Professor, Civil and Environmental Engineering

Email

jthomson@apl.washington.edu

Phone

206-616-0858

Research Interests

Environmental Fluid Mechanics, Ocean Surface Waves, Marine Renewable Energy (tidal and wave), Coastal and Nearshore Processes, Ocean Instrumentation

Biosketch

Dr. Thomson studies waves, currents, and turbulence by combining field observations and remote sensing techniques

Education

B.A. Physics, Middlebury College, 2000

Ph.D. Physical Oceanography, MIT/WHOI, 2006

Projects

Arctic PISCES

Pacific Infrastructure for Sustaining Continuous Engineering and Science in the Arctic. Advancing observing and prediction science in Arctic coastal and inner-shelf regions. Learn more and join the collaboration.

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14 Apr 2025

Arctic PISCES supports sub-seasonal to seasonal forecasts of the ice–ocean–atmosphere system by (1) monitoring conditions in the Arctic coastal zone and improving forecast models, and (2) tracking ocean heat content and its impact on the state of the landfast ice. Collaborating partners include APL-UW, Pacific Northwest National Laboratory, and the University of Alaska Fairbanks.

Persistent Measurements of Surface Waves in Landfast Ice Using Fiber Optic Telecommunication Cables

The high-resolution data collected during this research will help address fundamental questions about wave attenuation in landfast ice and breakup. The research is motivated by two questions: (1) What is the spatial and temporal variability of wave attenuation in landfast sea ice? (2) What drives landfast breakup? (Collaborative research with M. Smith, WHOI)

30 Aug 2023

Hurricane Coastal Impacts

APL-UW scientists are collaborating with 10 research teams to tackle the National Oceanographic Partnership Program (NOPP) project goals: to enable better understanding and predictive ability of hurricane impacts, to serve and protect coastal communities. The APL-UW team will contribute air-deployed buoys to provide real time observations of hurricane waves and wave forcing that can be ingested by modeling groups, improving forecasts and validating hindcasts.

14 Dec 2021

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Videos

Around the Americas — One Island One Ocean

The Laboratory celebrates the launch of the One Island One Ocean 14-month, 27,000-mile expedition to circumnavigate North and South America. We are partnering in scientific observations of the coastal zone from the equator to high latitudes and are supporting community outreach and education events in dozens of ports.

5 May 2025

microSWIFTs: Tiny Oceanographic Floats Measure Extreme Coastal Conditions

These small, inexpensive ocean drifters are the latest generation of the Surface Wave Instrument Float with Tracking (SWIFT) platform developed at APL-UW. They are being used in several collaborative research experiments to increase the density of nearshore wave observations.

19 Apr 2022

Using a Wave Energy Converter for UUV Recharge

This project demonstrates the interface required to operate, dock, and wirelessly charge an uncrewed underwater vehicle with a wave energy converter.

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11 Apr 2022

Uncrewed underwater vehicles (UUVs) predominantly use onboard batteries for energy, limiting mission duration based on the amount of stored energy that can be carried by the vehicle. Vehicle recharge requires recovery using costly, human-supported vessel operations. The ocean is full of untapped energy in the form of waves that, when converted to electrical energy by a wave energy converter (WEC), can be used locally to recharge UUVs without human intervention. In this project we designed and developed a coupled WEC-UUV system, with emphasis on the systems developed to interface the UUV to the WEC.

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Publications

2000-present and while at APL-UW

Buoy observation of high frequency ocean wave energy: Accuracy, consistency, and concerns for predictive applications

Rogers, W.E., and J. Thomson, "Buoy observation of high frequency ocean wave energy: Accuracy, consistency, and concerns for predictive applications," NRL Memorandum Report, NRL/7320/MR-2026/2, Naval Research Laboratory, Stennis Space Center, MS, March 2026, 48 pp.

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13 Mar 2026

Observational data from buoys are of primary importance during the development, calibration, and evaluation of ocean wave models, and
these data are also used to make real-time corrections to operational models via data assimilation. By association, systematic inaccuracies in
any buoy data are equally important, and thus when two buoy types provide systematically inconsistent information, this is a concern for
anyone using an ocean wave model. This report is concerned with the accuracy of the high frequency portion of the ocean wave spectrum
commonly observable by buoys, roughly 0.2 to 0.6 Hz. We evaluate four buoy types (two moored, two drifting) using two quantitative
measures. The first involves comparing each type with a co-located ocean wave model. The second method involves evaluation of high
frequency energy level as a function of wind speed. Both evaluation methods suggest that the Datawell Waverider (DWR) buoys have a
strong tendency to report higher energy levels than the other three buoy types. A possible explanation is the Doppler shift of the drifting
buoys and a damped response of the larger moored buoys. We evaluate high frequency energy level using three different metrics (mean
square slope, energy in a band of high frequencies, and spectral density at a single, specific band, 0.4 Hz), and the conclusions are found to
be insensitive to the parameter used.

Comparisons of seafloor distributed fiber-optic sensing datasets and empirical calibrations for inferring ocean surface gravity waves

Glover, H.E., M.M. Smith, M.E. Wengrove, E.F. Williams, J. Thomson, M.Ifju, and B.P. Lipovsky, "Comparisons of seafloor distributed fiber-optic sensing datasets and empirical calibrations for inferring ocean surface gravity waves," J. Atmos. Ocean. Technol., 43, 289-307, doi:10.1175/JTECH-D-24-0112.1, 2026.

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1 Mar 2026

Distributed acoustic sensing (DAS) is an emerging oceanographic technique in which an interrogator continuously records nanoscale strain of a fiber-optic cable, such as a telecommunication cable, with meter-scale measurement spacing over tens of kilometers. Empirical methods have recently been established for calculating pressure spectra to measure ocean surface gravity wave statistics from DAS strain. Here, we compile data from six submarine DAS experiments to provide a comparison between studies and establish recommendations for using DAS to measure ocean waves. Data were collected from Alaska, Hawaii, Massachusetts, North Carolina, and Oregon, United States, with different interrogators on different cable types in 0–60 m of water with 0–4 m of burial. Ground-truth measurements of ocean waves were provided by standard near-bed or sea surface instruments. The raw strain recorded in each experiment varied over four orders of magnitude, which could not be explained by water depth, wave conditions, or interrogator settings and suggests that cable characteristics and burial depth are important factors controlling strain magnitude and measurement quality. Strain spectra were converted to near-bed pressure spectra using a frequency-dependent, location-specific empirical correction factor, and DAS-derived pressure spectra were used to calculate wave statistics. The correction factors varied over 10 orders of magnitude between sites yet provided accurate calculations of wave height and period (root-mean-square error of 0.2–0.6 m for Hs and 0.2–1.6 s for Te and Tp). The volume of data necessary for calibration is discussed. This meta-analysis highlights future oceanographic applications of DAS.

Neural network-based methods for ocean surface wave measurement using submarine distributed acoustic sensing (DAS)

Davis, J.R., J. Thomson, M. Smith, and A.C. Stanciu, "Neural network-based methods for ocean surface wave measurement using submarine distributed acoustic sensing (DAS)," J. Geophys. Res., 3, doi:10.1029/2025JH001090, 2026.

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1 Feb 2026

Two new data-driven models for estimating ocean surface waves from distributed acoustic sensing (DAS) submarine cable strain rate are developed using supervised machine learning on a 10-day data set collected offshore of Oliktok Point, Alaska. The new models were trained on target data from seafloor pressure moorings at three sites spaced evenly along 27.1 km of cable and were benchmarked against an empirical transfer function method previously used to estimate waves from DAS. A model which uses convolutional neural networks to transform 2-km frequency-wavenumber strain spectra to seafloor pressure spectra outperforms the benchmark in wave height prediction (RMSE of 0.15 vs. 0.41 m) and period prediction (0.29 vs. 0.37 s) when evaluated on a held-out test data set. When applied to a DAS data set collected on the same cable 2 years prior, the CNN-based model maintained similar significant wave height performance (RMSE = 0.23 m) relative to available satellite altimetry data. A two-hidden-layer, fully connected neural network which transforms 1-D strain spectra to seafloor pressure spectra also outperforms the benchmark in wave height prediction (RMSE of 0.19 vs. 0.41 m), but does not generalize as well to the prior data. Regression-based machine learning is useful for estimating waves from DAS data when the pressure-strain relationship varies temporally and spatially across different wave conditions. Models can be applied to DAS data to measure waves with higher spatial resolution and longer temporal coverage than traditional methods, which often measure waves only at a single point.

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In The News

3 reasons why the Pacific Ocean tsunami fizzled before reaching WA

The Seattle Times, Conrad Swanson

The earthquake Tuesday off Russia's far eastern coast was one of the strongest in recorded history. People across the Pacific Ocean braced for a potentially devastating tsunami. It appears we've escaped largely unscathed. Scientists explain why by pointing to the earthquake's location and timing.

30 Jul 2025

Why physicists are air-dropping buoys into the paths of hurricanes

New Scientist, James Dinneen

A sprawling research program aims to improve hurricane forecasts by collecting data at the chaotic interface of ocean and atmosphere.

20 Sep 2024

NOAA researchers study sea ice retreat, link to harmful algal blooms

The Nome Nuggest, Colin A. Warren

Last week a team of National Oceanic and Atmospheric Administration researchers arrived in Nome to launch the third year of an investigation that seeks to study sea ice retreat and chart phytoplankton in the northern Bering Sea.

14 Jun 2024

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