Emerging ocean observations for interdisciplinary data assimilation systems

Identification, understanding, and prediction of many interdisciplinary oceanographic processes remain as elusive goals of ocean science. However, new ocean technologies are being effectively used to increase the variety and numbers of sampled variables and thus to fill in the gaps of the time-space continuum of interdisciplinary ocean observations. The formulation, accuracy, and efficacy of data assimilative models are highly dependent upon the quality and quantity of interdisciplinary observational data. In turn, the design of optimal sampling networks will benefit from data assimilative-based observation system simulation experiments (OSSEs). The present contribution, which is directed toward both modelers and observationalists, reviews emerging interdisciplinary observational capabilities and their optimal utilization in data assimilative models.