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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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Fang-Fang Li, Hui-Min Zuo, Yinghui 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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Fang-Fang Li, Ying-Hui Jia, Guang-Qian Wang, and Jun Qiu


Sound waves have proven to be effective in promoting the interaction and aggregation of droplets. It is necessary to theoretically study the motion of particles in a sound field to develop new acoustic technology for precipitation enhancement. In this paper, the motion of cloud droplets due to a traveling sound wave field emitted from the ground to the air is simulated using the motion equation of point particles. The force condition of the particles in the oscillating flow field is analyzed. Meanwhile, the effects of droplet size, sound frequency, and sound pressure level (SPL) on the velocity and displacement of the droplets are also investigated. The results show that Stokes force and gravity play a dominant role in the falling process of cloud droplets, and the effect of the sound wave is mainly reflected in the fluctuation of velocity and displacement, which also promotes the displacement of cloud droplets to a certain extent. The maximum displacement increments of cloud droplets of 10 µm can reach 9200 µm due to the action of sound waves of 50 Hz and 143.4 dB. The SPL required for a noticeable velocity fluctuation for droplets of 10 µm with frequency of 50 Hz is 88.2 dB. When SPL < 100 dB and frequency > 500 Hz, the effect is negligible. The cloud droplet size plays a significant role in the motion, and the sound action is weaker for larger particles. For a smaller sound frequency and higher SPL, the effect of the sound wave is more prominent.

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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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