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

• Published in: Ocean modelling (Oxford) Vol. 183; p. 102200
• Main Authors: Houghton, Isabel A., Penny, Stephen G., Hegermiller, Christie, Cesaretti, Moriah, Teicheira, Camille, Smit, Pieter B.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2023
• Subjects: Ensemble data assimilation; LETKF; Sofar Spotter wave buoys; Wave data assimilation; Wave data assimilation; LETKF; Ensemble data assimilation; Sofar Spotter wave buoys
• ISSN: 1463-5003; 1463-5011
• DOI: 10.1016/j.ocemod.2023.102200

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 highlight 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). •LETKF improves global wave forecast skill out to 2.5 days.•LETKF is more effective far from observations than optimal interpolation.•The ensemble-based method enables future coupled DA across atmosphere-ocean-wave.