A dynamic averaging algorithm combined with a high-order spectral (HOS) model is introduced for wave reconstruction and prediction using radar data from stationary or moving ships. The algorithm enables gradual reconstruction of waves entering the radar's blind zone and improves accuracy by reducing the effects of shadowing modulation, particularly under steep wave conditions. The effects of nonlinearity on wave prediction accuracy are examined across a range of sea states with varying wave steepness and directional spreading angles. Results show that the HOS model, which includes third-order nonlinear effects, outperforms both linear and second-order models. Its advantage is especially clear in steep or narrowly spread wave fields, where simpler models fail to accurately predict the timing and height of large waves, leading to wave prediction errors at the ship's position up to three times higher than those from the HOS model. Additionally, when the ship slows down in head waves or speeds up in following waves, the relative velocity between the waves and the ship decreases. This reduction in relative speed extends both the time required for accurately reconstructing waves in the blind zone and the length of the predictable wave field ahead of the ship.

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• Hydrodynamic waves
• Surface gravity waves
• Euler equation