Data for: Ensemble-based data assimilation of significant wave height from Sofar Spotters and satellite altimeters with a global operational wave model

An ensemble-based method for wave data assimilation is implemented using
significant wave height observations from the globally distributed network
of Sofar Spotter buoys and satellite altimeters. The Local Ensemble
Transform Kalman Filter (LETKF) method generates skillful analysis fields
resulting in reduced forecast errors out to 2.5 days when used as initial
conditions in a cycled wave data assimilation system. The LETKF method
provides more physically realistic model state updates that better reflect
the underlying sea state dynamics and uncertainty compared to methods such
as optimal interpolation. Skill assessment far from any included
observations and inspection of specific storm events highlights the
advantages of LETKF over an optimal interpolation method for data
assimilation. This advancement has immediate value in improving
predictions of the sea state and, more broadly, enabling future coupled
data assimilation and utilization of global surface observations across
domains (atmosphere-wave-ocean).