Operational assimilation of spectral wave data from the Sofar Spotter network

Historically, the sparseness of in situ open-ocean wave and weather
observations has severely limited the forecast skill of weather over the
ocean with major social and economic consequences for coastal communities
and maritime industries. Ocean surface waves, specifically, are important
for the interaction between atmosphere and ocean, and thus key in modeling
weather and climate processes. Here, we investigate the improvements
achievable from a large distributed sensor network combined with advances
in assimilation strategies. Wave spectra from a global network of over 600
Sofar Spotter buoys are assimilated into an operational global wave
forecast via optimal interpolation to update model spectra to best fit
observations. We demonstrate end-to-end improvements in forecast skill of
significant wave height of 38%, and up to 45% for other bulk parameters.
This shows distributed observations of the air-sea interface, with
advances in assimilation strategies, can reduce uncertainty in forecasts
to dramatically improve earth system modeling