A multiple-location modeling scheme for physics-regularized networks: Recurrent forecasting of fixed-location buoy observations

Reliable oceanic and climate analysis depend on high-quality sensor readings, yet these systems commonly encounter significant sensor limitations, leading to missing data. Addressing this issue is critical for ensuring accurate forecasts and analyses. In this work, the data gap problem is studied by developing physics-regularized machine learning models with multiple-location modeling to forecast missing sensor data. Utilized are recurrent statistical surrogate models that generate hourly 24-hour forecasts. To train these models, we use a selection of five sensor features collected over three years. Introduced is a multi-location modeling scheme that uniquely combines sensor data from nearby buoys as a novel methodology. This approach allows for more stable and accurate predictions compared to forecasting with single buoy data alone. Our experiments reveal that grouping six buoys yields the best forecasting performance. Furthermore, we improve model accuracy by integrating buoy data with numerical ocean models and applying a physicsregularized loss function. This technique mitigates the impact of missing or erratic data, leading to more dependable 24hour forecasts. Our findings demonstrate that the combination of multiple-location modeling and physics-based regularization enhances the stability and accuracy of oceanic data forecasting.