Surfzone State Estimation, with Applications to Quadcopter-Based Remote Sensing Data

TitleSurfzone State Estimation, with Applications to Quadcopter-Based Remote Sensing Data
Publication TypeJournal Article
Year of Publication2018
AuthorsWilson, G, Berezhnoy, S
JournalJournal of Atmospheric and Oceanic Technology
Type of ArticleJournal Article
KeywordsBayesian methods, Coastal flows, Data assimilation, Inverse methods, Numerical analysis/modeling, Variational analysis

A one-dimensional variational data assimilation (1DVar) method is presented based on the depth- and time-averaged alongshore-uniform surfzone wave and current equations, for simultaneous estimation of three uncertain variables: bathymetry, incident wave boundary conditions, and bed roughness. The method is validated using twin tests and in situ field observations, and its results are shown to be comparable to those of an existing ensemble-based bathymetry inversion technique. Unlike existing techniques, the ability to simultaneously estimate boundary conditions and bed roughness along with bathymetry also means the 1DVar method can produce full state estimates without the requirement for additional supporting measurements (e.g., direct measurements of the incident waves). A proof-of-concept field application is shown using observations collected from an unmanned quadcopter sensor package that measures surfzone wave height from a fixed-beam lidar range finder, and time-averaged longshore current from particle image velocimetry of drifting surface foam.


Coastal Endurance