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Roxanne Carini

Postdoctoral Scholar



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

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.

A data-driven approach to classifying wave breaking in infrared imagery

Buscombe, D., and R.J. Carini, "A data-driven approach to classifying wave breaking in infrared imagery," Remote Sens., 11, 859, doi:10.3390/rs11070859, 2019.

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10 Apr 2019

We apply deep convolutional neural networks (CNNs) to estimate wave breaking type (e.g., non-breaking, spilling, plunging) from close-range monochrome infrared imagery of the surf zone. Image features are extracted using six popular CNN architectures developed for generic image feature extraction. Logistic regression on these features is then used to classify breaker type. The six CNN-based models are compared without and with augmentation, a process that creates larger training datasets using random image transformations. The simplest model performs optimally, achieving average classification accuracies of 89% and 93%, without and with image augmentation respectively. Without augmentation, average classification accuracies vary substantially with CNN model. With augmentation, sensitivity to model choice is minimized. A class activation analysis reveals the relative importance of image features to a given classification. During its passage, the front face and crest of a spilling breaker are more important than the back face. For a plunging breaker, the crest and back face of the wave are most important, which suggests that CNN-based models utilize the distinctive ‘streak’ temperature patterns observed on the back face of plunging breakers for classification.

Estimating wave energy dissipation in the surf zone using thermal infrared imagery

Carini, R.J. C.C. Chickadel, A.T. Jessup, and J. Thomson, "Estimating wave energy dissipation in the surf zone using thermal infrared imagery," J. Geophys. Res., 120, 3937-3957, doi:10.1002/2014JC010561, 2015.

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1 Jun 2015

Thermal infrared (IR) imagery is used to quantify the high spatial and temporal variability of dissipation due to wave breaking in the surf zone. The foam produced in an actively breaking crest, or wave roller, has a distinct signature in IR imagery. A retrieval algorithm is developed to detect breaking waves and extract wave roller length using measurements taken during the Surf Zone Optics 2010 experiment at Duck, NC. The remotely derived roller length and an in situ estimate of wave slope are used to estimate dissipation due to wave breaking by means of the wave-resolving model by Duncan (1981). The wave energy dissipation rate estimates show a pattern of increased breaking during low tide over a sand bar, consistent with in situ turbulent kinetic energy dissipation rate estimates from fixed and drifting instruments over the bar. When integrated over the surf zone width, these dissipation rate estimates account for 40–69% of the incoming wave energy flux. The Duncan (1981) estimates agree with those from a dissipation parameterization by Janssen and Battjes (2007), a wave energy dissipation model commonly applied within nearshore circulation models.

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