  This study investigated an approach to improve the accuracy of
computationally lightweight surrogate models by updating forecasts based on
historical accuracy relative to sparse observation data. Using a lightweight,
ocean-wave forecasting model, we created a large number of model ensembles,
with perturbed inputs, for a two-year study period. Forecasts were aggregated
using a machine-learning algorithm that combined forecasts from multiple,
independent models into a single "best-estimate" prediction of the true state.
The framework was applied to a case-study site in Monterey Bay, California.
A~learning-aggregation technique used historical observations and model
forecasts to calculate a weight for each ensemble member. Weighted ensemble
predictions were compared to measured wave conditions to evaluate performance
against present state-of-the-art. Finally, we discussed how this framework,
which integrates ensemble aggregations and surrogate models, can be used to
improve forecasting systems and further enable scientific process studies.
