Hell, M. C., B. D. Cornuelle, S. Gille, A. J. Miller and P. D. Bromirski, 2019:
Identifying ocean swell generation events from Ross Ice Shelf seismic data
Journal of Atmospheric and Oceanic Technology, 36, 2171-2189.
Abstract.
Strong surface winds under extra-tropical cyclones exert intense surface
stresses on the ocean that lead to upper ocean mixing, intensified heat fluxes
and the generation of waves, that, over time, lead to swell waves (longer than
10 s period) that travel long distances. Because low-frequency swell propagates
faster than high-frequency swell, the frequency dependence of swell arrival
times at a measurement site can be used to infer the distance and time that
the wave has traveled from its generation site. This study presents a methodology
that employs spectrograms of ocean swell from point observations on the
Ross Ice Shelf (RIS) to verify the position of high wind speed areas over the
Southern Ocean, and therefore of extra-tropical cyclones. The focus here is
on the implementation and robustness of the methodology in order to lay the
groundwork for future broad application to verify Southern Ocean storm positions
from atmospheric reanalysis data. The method developed here combines
linear swell dispersion with a parametric wave model to construct a time and
frequency-dependent model of the dispersed swell arrivals in spectrograms
of seismic observations on the RIS. A two-step optimization procedure (deep
learning) of gradient descent and Monte Carlo sampling allows detailed estimates
of the parameter distributions, with robust estimates of swell origins.
Median uncertainties of swell source locations are 450 km in radial distance
and 2 hours in time. The uncertainties are derived from RIS observations and
the model, rather than an assumed distribution. This method is an example of
supervised machine learning informed by physical first principles in order to
facilitate parameter interpretation in the physical domain.
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