A long-standing problem in maritime operations and ocean development projects has been the prediction of instantaneous wave energy. Wave measurements collected using an array of freely drifting arrays of Surface Wave Instrument Float with Tracking (SWIFT) buoys are used to test methods for phase-resolved wave prediction in a wide range of observed sea states. Using a linear inverse model in directionally-rich, broadbanded wave fields can improve instantaneous heave predictions by an average of 63% relative to statistical forecasts based on wave spectra. Numerical simulations of a Gaussian sea, seeded with synthetic buoys, were used to supplement observations and characterize the spatiotemporal extent of reconstruction accuracy. Observations and numerical results agree well with theoretical deterministic prediction zones proposed in previous studies and suggest that the phase-resolved forecast horizon is between 1–3 average wave periods for a maximum measurement interval of 10 wave periods for ocean wave fields observed during the experiment. Prediction accuracy is dependent on the geometry and duration of the measurements and is discussed in the context of the nonlinearity and bandwidth of incident wave fields.

A distributed sensor network of over 100 free-drifting, real-time marine weather sensors was deployed in the Pacific Ocean beginning in early 2019. The Spotter buoys used in the network represent a next-generation ocean weather sensor designed to measure surface waves, wind, currents, and sea surface temperature. Large distributed sensor networks like these provide much needed long-dwell sensing capabilities in open-ocean regions. Despite the demand for better weather forecasts and climate data in the oceans, direct in situ measurements of marine surface weather (waves, winds, currents) remain exceedingly sparse in the open oceans. Because of the large expanse of Earth’s oceans, distributed paradigms are necessary to create sufficient data density at global scale, similar to advances in sensing on land and in space. Here we discuss initial findings from this long-dwell open-ocean distributed sensor network. Through triple-collocation analysis, we determine errors in collocated satellite-derived observations and model estimates. The correlation analysis shows that the Spotter network provides wave height data with lower errors than both satellites and models. The wave spectrum was also further used to infer wind speed. Buoy drift dynamics are similar to established drogued drifters, particularly when accounting for windage. We find a windage correction factor for the Spotter buoy of approximately 1%, which is in agreement with theoretical estimates. Altogether, we present a completely new open-ocean weather dataset and characterize the data quality against other observations and models to demonstrate the broad value for ocean monitoring and forecasting that can be achieved using large-scale distributed sensor networks in the oceans.