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

This data contains the collocated model and Spotter observations of bulk parameters of the wave forecast used to assess the average (rmse) skill of the data assimilation strategies as a function of lead time. It also contains the partitioned sea vs. swell data following the method described in the supplement and in the README.txt.

The csv files can most easily be read into a Pandas dataframe in Python but any spreadsheet viewer will work.