Toward Dynamic Data-Driven Systems for Rapid Adaptive Interdisciplinary Ocean Forecasting

Abstract

The state of the ocean evolves and its dynamics involves transitions oc- curring at multiple scales. For efficient and rapid interdisciplinary forecasting, ocean observing and prediction systems must have the same behavior and adapt to the ever- changing dynamics. The work discussed here aims to set the basis of a distributed system for real-time interdisciplinary ocean field and uncertainty forecasting with adaptive modeling and adaptive sampling. The scientific goal is to couple physical and biological oceanography with ocean acoustics. The technical goal is to build a dynamic system based on advanced infrastructures, distributed / grid computing and efficient information retrieval and visualization interfaces. Importantly, the system combines a suite of modern legacy physical models, acoustic models and ocean cur- rent monitoring data assimilation schemes with new adaptive modeling and adap- tive sampling methods. The legacy systems are encapsulated at the binary level us- ing software component methodologies. Measurement models are utilized to link the observed data to the dynamical model variables and structures. With adaptive sampling, the data acquisition is dynamic and aims to minimize the predicted un- certainties, maximize the sampling of key dynamics and maintain overall coverage. With adaptive modeling, model improvements are dynamic and aim to select the best model structures and parameters among different physical or biogeochemical parameterizations. The dynamic coupling of models and measurements discussed here represents a Dynamic Data-Driven Application System (DDDAS). Technical and scientific progress is highlighted based on examples in Massachusetts Bay, and Monterey Bay and the California Current System.