TOWARDS A DYNAMIC DATA DRIVEN SYSTEM FOR RAPID ADAPTIVE INTERDISCIPLINARY OCEAN FORECASTING
Ruhao Tian · 2004
The state of the ocean evolves and its dynamics involves transitions occurring on 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 present research 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 data assimilation schemes with new adaptive modeling and adaptive sampling software. The legacy systems are encapsulated at the binary level using 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 uncertainties, 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. This presentation outlines and illustrates the concept, architecture and components of such a Dynamic Data Driven Application System (DDDAS). Current technical and scientific progress is highlighted based on examples in Massachusetts Bay, and Monterey Bay and the California Current System.