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  • Author or Editor: Jia Wang x
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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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Fang-Fang Li, Hui-Min Zuo, Ying-Hui Jia, Qi Wang, and Jun Qiu


All-sky images derived from ground-based imaging equipment have become an important means of recognizing and quantifying cloud information. Accurate cloud detection is a prerequisite for obtaining important cloud information from an all-sky image. Existing cloud segmentation algorithms can achieve high accuracy. However, for different scenes, such as completely cloudy with obscured sun and partly cloudy with unobscured sun, the use of specific algorithms can further improve segmentation. In this study, a hybrid cloud detection algorithm based on intelligent scene recognition (HCD-ISR) is proposed. It uses suitable cloud segmentation algorithms for images in different scenes recognized by ISR, so as to utilize the various algorithms to their full potential. First, we developed an ISR method to automatically classify the all-sky images into three scenes. In scene A, the sky is completely clear; in scene B, the sky is partly cloudy with unobscured sun; and in scene C, the sun is completely obscured by clouds. The experimental results show that the ISR method can correctly identify 93% of the images. The most suitable cloud detection algorithm was selected for each scene based on the relevant features of the images in that scene. A fixed thresholding (FT) method was used for the images in scene C. For the most complicated scene, that is, scene B, the clear-sky background difference (CSBD) method was used to identify cloud pixels based on a clear-sky library (CSL). The images in the CSL were automatically filtered by ISR. Compared to FT, adaptive thresholding (AT), and CSBD methods, the proposed HCD-ISR method has the highest accuracy (95.62%). The quantitative evaluation and visualization results show that the proposed HCD-ISR algorithm makes full use of the advantages of different cloud detection methods, and is more flexible and robust.

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