A Data Assimilation System for Costal Ocean Real-Time Predictions

Zhijin Li, Yi Chao
Jet Propulsion Laboratory, California Institute of Technology
James C. McWilliams
University of California, Los Angeles
Kayo Ide
University of Maryland, College Park3


A coastal ocean observing system generally consists of sparse conventional observations and high resolution satellite and radar remote sensing measurements. Difficulties arise when observations from such an observing system are assimilated into high resolution models, in particular, into those regional and coastal ocean models that have been using spatial resolutions approaching 1 km and thus have the capability of representing flow systems over a wide range of spatial scales. A multi-scale three-dimensional variational data assimilation (MS-3DVAR) scheme has been formulated. This scheme is characterized by constructing error covariance at different spatial scales and implementing data assimilation sequentially from large to small scales. The application of this scheme to real-time coastal ocean observing systems will be presented.