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Roxanne Carini

Senior Oceanographer





Research Interests

Coastal Hydrodynamics, Nearshore Wave Physics, Coastal Hazards, Remote Sensing


Roxanne Carini is a Research Associate with NANOOS, the Northwest Association of Networked Ocean Observing Systems, working to provide Pacific Northwest stakeholders with high-quality ocean and coastal data, tools, and information they need to make responsive and responsible decisions about safety, livelihoods, and stewardship.

During her Ph.D. at the University of Washington, Carini conducted research at the intersection of physical oceanography and coastal engineering. Based in the APL-UW AIRS Department, she used remote sensing technologies to observe and quantify breaking waves in the surf zone to better understand how wave forces change the coastal environment.

Following her Ph.D., Dr. Carini served as a Knauss Marine Policy Fellow at the U.S. Marine Mammal Commission. There, she worked broadly on science, policy, and communications issues, with projects including coordinating interagency reporting of federally funded marine mammal research and briefing Congressional offices on policy-relevant marine mammal science. Dr. Carini combines her research expertise, science policy communication skills, and call to serve the PNW community in her position with NANOOS.

Department Affiliation

Ocean Physics


B.S. Applied Mathematics, Yale University, 2011

M.S.C.E. Civil & Environmental Engineering, University of Washington, 2014

Ph.D. Civil & Environmental Engineering, University of Washington, 2019


2000-present and while at APL-UW

Surf zone waves at the onset of breaking: 1. LIDAR and IR data fusion methods

Carini, R.J., C.C. Chickadel, and A.T. Jessup, "Surf zone waves at the onset of breaking: 1. LIDAR and IR data fusion methods," J. Geophys. Res., 126, doi:10.1029/2020JC016934, 2021.

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1 Apr 2021

This is the first of a 2‐part series concerning remote observation and wave‐by‐wave analysis of the onset of breaking in the surf zone. In the surf zone, breaking waves drive nearshore circulation, suspend sediment, and promote air–sea gas exchange. Nearshore wave model predictions often diverge from in situ measurements near the break point location because common parameterizations do not account for the rapid changes that occur near the onset of breaking. This work presents extensive methodology to combine data from a line‐scanning LIDAR and thermal infrared cameras to detect breaking, classify breaker type, and measure geometric wave parameters on a wave‐by‐wave basis, which can be used to improve breaker parameterizations. Over 2,600 non‐breaking and 1,600 breaking waves are analyzed from data collected at the USACE Field Research Facility in Duck, NC, including 413 spilling and 111 plunging waves for which the onset of breaking was observed. Wave height is estimated using a spatio‐temporal method for wave tracking that preserves the sea surface elevation maximum and overcomes field of view limitations. Methods for estimating instantaneous wave speed are refined by fitting a skewed Gaussian function to each wave profile before tracking the peaks. Wave slope is estimated from a linear fit to the upper 80% of the wave face, which provides a robust metric and strong correlation with geometric wave slope defined relative to mean sea level. Finally, breaking wave face foam coverage is analyzed to assess common model assumptions about roller length for wave energy dissipation parameterizations.

Surf zone waves at the onset of breaking: 2. Predicting breaking and breaker type

Carini, R.J., C.C. Chickadel, and A.T. Jessup, "Surf zone waves at the onset of breaking: 2. Predicting breaking and breaker type," J. Geophys. Res., 126, doi:10.1029/2020JC016935, 2021.

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1 Apr 2021

This is the second of a two‐part series concerning remote observation and wave‐by‐wave analysis of the onset of breaking for spilling and plunging waves in the surf zone. Nearshore phase‐averaged and phase‐resolving wave models parameterize and directly simulate wave breaking and require realistic critical values of key wave parameters, such as the depth‐limited breaking index γ, steepness, or phase speed to initialize wave breaking. Using LIDAR line‐scans and infrared imagery, we observe over 1,600 breaking waves at the US Army Corps of Engineers Field Research Facility (FRF) in Duck, NC, and examine these parameters on a wave‐by‐wave basis at the onset of breaking for 413 spilling and 111 plunging waves. We find that γ is maximum near the onset of breaking at values consistent with those previously observed at the FRF, but that γ for plunging waves (0.73 ≤ γP ≤ 0.81) is greater than γ for spilling waves (0.63 ≤ γS ≤ 0.71). Direct estimates of wave face slope are maximum at the onset of breaking, approximately 22° for spilling and 30° for plunging waves. Using the relationship between γ and wave face slope, we develop a threshold for the onset of breaking that is a linear function of the two parameters. Wave face slope and γ are further used together to quantify whether a spilling‐ or plunging‐type breaker is more likely. We test the Miche steepness limit on our depth‐limited breaking data and find it correctly predicts only 10% of the plunging breakers and none of the spilling breakers in the surf zone.

Optical wave gauging using deep neural networks

Buscombe, D., R.J. Carini, S.R. Harrison, C.C. Chickadel, and J.A. Warrick, "Optical wave gauging using deep neural networks," Coastal Eng., 155, 103593, doi:10.1016/j.coastaleng.2019.103593, 2020.

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1 Jan 2020

We develop a remote wave gauging technique to estimate wave height and period from imagery of waves in the surf zone. In this proof-of-concept study, we apply the same framework to three datasets: the first, a set of close-range monochrome infrared (IR) images of individual nearshore waves at Duck, NC, USA; the second, a set of visible (i.e. RGB) band orthomosaics of a larger nearshore area near Santa Cruz, CA, USA; and the third, a set of oblique (unrectified) images from the same site. The network is trained using coincident images and in situ wave measurements. The optical wave gauge (OWG) consists of a deep convolutional neural network (CNN) to extract features from imagery — called a ‘base model’, with additional layers to distill the feature information into lower dimensional spaces, and a final layer of dense neurons to predict continuously varying quantities. Four base models are compared. The OWG is trained for both individual wave height and period, and statistical quantities like significant wave height and peak wave period. The best performing OWG on the IR dataset achieved RMS errors of 0.14 m and 0.41 s for height and period, respectively, capturing up to 98% of the variance in these quantities. The best performing OWG on the visible band rectified dataset achieved RMS errors of 0.08 m and 0.79 s, respectively, for height and period. The same values for the oblique RGB imagery were 0.11 m and 0.81 s for height and period, respectively. Overall, wave height and period accuracy is sensitive to choice of base model; OWGs built upon MobilenetV2 tend to perform worst and those built on Inception-ResnetV2 have the smallest RMS error. The presence or otherwise of residual layers in the model makes little systematic difference to the final OWG accuracy. Smaller batch sizes used in model training tend to result in more accurate OWGs. An out-of-calibration validation, using images associated with wave heights or periods outside the range of values represented in the training data, showed that the ability for OWGs to predict the bottom 5% of low wave heights and the top 5% of high wave heights was reasonably good, but the same was not generally true of wave period. The same framework, not optimized for either dataset, predicts both quantities with high accuracy when trained on imagery, despite the differences in electromagnetic band, perspective, and scale. The OWG estimates wave properties from an image in less than 100 ms on a modestly sized CPU, allowing for the possibility of continuous real-time wave estimates.

More Publications

Acoustics Air-Sea Interaction & Remote Sensing Center for Environmental & Information Systems Center for Industrial & Medical Ultrasound Electronic & Photonic Systems Ocean Engineering Ocean Physics Polar Science Center