The state of the ocean evolves and its dynamics involves transitions occurring 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. This chapter sets 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 acoustic measurements. The technical goal is to build a dynamic modeling and instrumentation system based on advanced infrastructures, distributed/grid computing, and efficient information retrieval and visualization interfaces, from which all these are incorporated into the Poseidon system. Importantly, the Poseidon system combines a suite of modern legacy physical models, acoustic models, and ocean current monitoring data assimilation schemes with innovative modeling and adaptive sampling methods. 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 optimized sampling of key dynamics, and maintain overall coverage. With adaptive modeling, model improvements dynamically select the best model structures and parameters among different physical or biogeochemical parameterizations. The dynamic coupling of models and measurements discussed here, and embodied in the Poseidon system, represents a Dynamic Data-Driven Applications Systems (DDDAS). Technical and scientific progress is highlighted based on examples in Massachusetts Bay, Monterey Bay, and the California Current System.