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Chris Chickadel

Principal Oceanographer

Affiliate Assistant Professor, Civil and Environmental Engineering

Email

chickadel@apl.washington.edu

Phone

206-221-7673

Education

B.S. Oceanography, University of Washington, 1997

M.S. Oceanography, Oregon State University, 2003

Ph.D. Oceanography, Oregon State University, 2007

Projects

Inner Shelf Dynamics

The inner shelf region begins just offshore of the surf zone, where breaking by surface gravity waves dominate, and extends inshore of the mid-shelf, where theoretical Ekman transport is fully realized. Our main goal is to provide provide improved understanding and prediction of this difficult environment. This will involve efforts to assess the influence of the different boundaries — surf zone, mid and outer shelf, air-water interface, and bed — on the flow, mixing and stratification of the inner shelf. We will also gain information and predictive understanding of remotely sensed surface processes and their connection to processes in the underlying water column.

15 Dec 2015

COHerent STructures in Rivers and Estuaries eXperiment

The experiment is a four-year collaborative project that couples state-of-the-art remote sensing and in situ measurements with advanced numerical modeling to characterize coherent structures in river and estuarine flows.

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Coherent structures are generated in rivers and estuaries when the flow interacts with bathymetric and coastline features or when density stratification causes a gradient in surface properties. These coherent structures produce surface signatures that can be detected and quantified using remote sensing techniques. A second objective of this project is to determine the extent to which these remotely sensed signatures can be used to initialize and guide predictive models.

The study site selected for Year 1 and Year 2 field operations was the Snohomish River in Everett, WA. Its annual mean flow of approximately 300 cubic meters per second is the third largest discharge into Puget Sound. The mouth of the river is defined by the city of Everett to the west (man-influenced) and Jetty Island to the east (natural). The river is dredged to a nominal depth of 5 m from the mouth at the south end of Jetty Island to approximately 12 km upstream, while the undredged depth is nominally 1-3 m. Thus the river profile is a compound channel, with the full 300 m width at Jetty Island containing the dredged channel of about 50 m width. The tidal forcing is strong, with the tidal range representing up to 2/3 of the river%u2019s mean depth. There is a bypass between the north end of Jetty Island and the mainland that connects to a mudflat area. During high tides, the river flow bifurcates between the main channel and this bypass, while at low tide very little flow occurs in the bypass. A sill extends from the north tip of Jetty Island to the southeast toward the opposite bank. The depth along this sill varies from 2 m to 5 m and terminates in a large scour hole in the middle of the channel with a depth of about 10 m.

This research is being conducted by a partnership of experts in remote sensing, numerical modeling, and estuarine dynamics from the University of Washington (Applied Physics Laboratory, Civil and Environmental Engineering, and Oceanography) and Stanford University (Environmental Fluid Mechanics Laboratory). The program is funded by a Multidisciplinary University Research Initiative (MURI) grant sponsored by the Office of Naval Research.

Tidal Flats

Under an ONR-sponsored Department Research Initiative researchers are studying thermal signatures of inter-tidal sediments. The goal is to understand how sediment properties feedback on morphology and circulation, and the extent to which such properties
can be sensed remotely.

 

Publications

2000-present and while at APL-UW

The Inner-Shelf Dynamics Experiment

Kumar, N., and 49 others, including J. Thomson, M. Moulton, and C. Chickadel, "The Inner-Shelf Dynamics Experiment," Bull. Am. Meteorol. Soc., EOR, doi:10.1175/BAMS-D-19-0281.1, 2020.

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31 Dec 2020

The inner shelf, the transition zone between the surf zone and the mid shelf, is a dynamically complex region with the evolution of circulation and stratification driven by multiple physical processes. Cross-shelf exchange through the inner shelf has important implications for coastal water quality, ecological connectivity, and lateral movement of sediment and heat. The Inner-Shelf Dynamics Experiment (ISDE) was an intensive, coordinated, multi-institution field experiment from Sep.–Oct. 2017, conducted from the mid shelf, through the inner shelf and into the surf zone near Point Sal, CA. Satellite, airborne, shore- and ship-based remote sensing, in-water moorings and ship-based sampling, and numerical ocean circulation models forced by winds, waves and tides were used to investigate the dynamics governing the circulation and transport in the inner shelf and the role of coastline variability on regional circulation dynamics. Here, the following physical processes are highlighted: internal wave dynamics from the mid shelf to the inner shelf; flow separation and eddy shedding off Point Sal; offshore ejection of surfzone waters from rip currents; and wind-driven subtidal circulation dynamics. The extensive dataset from ISDE allows for unprecedented investigations into the role of physical processes in creating spatial heterogeneity, and nonlinear interactions between various inner-shelf physical processes. Overall, the highly spatially and temporally resolved oceanographic measurements and numerical simulations of ISDE provide a central framework for studies exploring this complex and fascinating region of the ocean.

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.

Bias correction of airborne thermal infrared observations over forests using melting snow

Pestana, S., C.C. Chickadel, A. Harpold, T.S. Kostadinov, H. Pai, S. Tyler, C. Webster, and J.D. Lundquist, "Bias correction of airborne thermal infrared observations over forests using melting snow," Water Resour. Res., 55, 11,331-11,343, doi:10.1029/2019WR025699, 2019.

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1 Dec 2019

Uncooled thermal infrared (TIR) imagers, commonly used on aircraft and small unmanned aircraft systems (UAS, "drones"), can provide high‐resolution surface temperature maps, but their accuracy is dependent on reliable calibration sources. A novel method for correcting surface temperature observations made by uncooled TIR imagers uses observations over melting snow, which provides a constant 0°C reference temperature. This bias correction method is applied to remotely sensed surface temperature observations of forests and snow over two mountain study sites: Laret, Davos, Switzerland (27 March 2017) in the Alps, and Sagehen Creek, California, USA (21 April 2017) in the Sierra Nevada. Surface temperature retrieval errors that arise from temperature‐induced instrument bias, differences in image resolution, retrieval of mixed pixels, and variable view angles were evaluated for these forest snow scenes. Applying the melting snow‐based bias correction decreased the root‐mean‐square error by about 1°C for retrieving snow, water, and forest canopy temperatures from airborne TIR observations. The influence of mixed pixels on surface temperature retrievals over forest snow scenes was found to depend on image resolution and the spatial distribution of forest stands. Airborne observations over the forests at Sagehen showed that near the edges of TIR images, at more than 20° from nadir, the snow surface within forest gaps smaller than 10 m was obscured by the surrounding trees. These off‐nadir views, with fewer mixed pixels, could allow more accurate airborne and satellite‐based observations of canopy surface temperatures.

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
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