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Jia Wang


This study describes the establishment of a Nowcast/Forecast System for Coastal Ocean Circulation (NFS-COC), which was run operationally on a daily basis to provide users ocean surface currents and sea levels that vary with synoptic winds, and seasonal and mesoscale variability intrinsic to the Florida Current. Based on the requirements of users, information about possible oil spills, trajectories, etc., is also provided by NFS-COC.

NFS-COC consists of two parts: a 3D ocean nowcast/forecast circulation model, Princeton Ocean Model (POM), and a 2D trajectory model. POM is automatically run to forecast ocean variables for up to 2 days under forcing of the Florida Current inflow/outflow and the predicted surface winds, which are automatically transferred (by ftp) from a file server at the National Meteorological Center (now known as the National Centers for Environmental Prediction). The winds from the mesoscale Eta Model are called Eta winds. Then the trajectory model is run to predict the path due to 1) the POM-predicted ocean surface currents, 2) wind drift due to the predicted Eta winds, and 3) turbulent dispersion based on a random flight (Markov process) model. The predicted surface trajectories can be used to estimate the physical transport of oil spills (and other drifting or floating objects) in the Straits of Florida and many other coastal seas. A simple data assimilation scheme (nudging to the volume transport) is designed into the NFS-COC, although some powerful data assimilation methods exist for assimilating other physical variables.

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Wenjing Jia, Dong Wang, Nadia Pinardi, Simona Simoncelli, Andrea Storto, and Simona Masina


A quality control (QC) procedure is developed to estimate monthly mean climatologies from the large Argo dataset (2005–12) over the North Pacific western boundary current region. In addition to the individual QC procedure, which checks for instrumental, transmission, and gross errors, the paper describes and shows the impact of climatological checks (collective QC) on the quality of both processed profiles and resultant climatological distributions. Objective analysis (OA) is applied progressively to produce the gridded climatological fields. The method uses horizontal regional climatological averages defined in five regime-oriented subregions in the Kuroshio area and the Japan Sea. Performing the QC procedure on specific coherent subregions produces improved profiling data and climatological fields because more details about the local hydrodynamics are taken into consideration. Nonrepresentative data and random noises are more effectively rejected by this method, which has value both in defining a climatological mean and identifying outlier data. Assessing with both profiling and coordinated datasets, the agreement is reasonably good (particularly for those areas with abundant observations), but the results (although already smoothed) can capture more detailed or mesoscale features for further regional studies. The method described has the potential to meet future challenges in processing accumulating Argo observations in the coming decades.

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