Operational Assimilation of Spectral Wave Data From the Sofar Spotter Network

• Published in: Geophysical research letters 2022-08, Vol.49 (15), p.n/a
• Main Authors: Houghton, Isabel A., Hegermiller, Christie, Teicheira, Camille, Smit, Pieter B.
• Format: Article
• Language: English
• Subjects: Accuracy; air‐sea interface; Assimilation; Atmospheric models; Buoy measurements; Buoys; Climate models; data assimilation; Directional spectra; distributed sensing; Drifting buoys; Economic forecasting; Economics; Forecast accuracy; Instruments; Interpolation; Marine energy; Meteorological data; Modelling; Numerical forecasting; Numerical forecasting models; Ocean models; Ocean surface; Ocean waves; Oceans; Renewable energy; Renewable resources; Resource management; Sensors; Significant wave height; Sofar; Spectra; Surface dynamics; Surface waves; wave buoys; Wave data; Wave height; wave modeling; Wave spectra; wave spectra observations; Weather; Weather forecasting
• ISSN: 0094-8276; 1944-8007
• DOI: 10.1029/2022GL098973

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. Plain Language Summary Historically, wave and weather observations are very sparse in the open ocean due to the cost and complexity of instruments and deployments. This lack of real‐time weather information results in low‐fidelity forecasts. Technological advances have led to the development of the Sofar sensor network, a distributed weather network spanning all the major oceans, consisting of over 600 free‐drifting buoys that measure the ocean surface dynamics in great detail (including wave directional spectra). In this work we investigate how such large networks can be successfully used to meaningfully improve forecast accuracy using a new assimilation strategy to ingest the data into operational numerical forecast models. We show substantial improvements in forecast accuracy of the ocean wavefield, which has broad implications for earth system modeling and will be directly relevant to coastal communities, marine renewable energy operations, and the efficiency of other maritime industries. Key Points A global network of over 600 drifting surface buoys reporting directional wave spectra every hour has been established Assimilation of wave spectra yields quantifiable wave forecast improvements over traditional assimilation using significant wave height Data from a new global ocean sensor and advances in wave data assimilation provide a direct path to improved marine weather forecasts