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  • Author or Editor: Wei Zhao x
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Zhou Shenghui
,
Wei Ming
,
Wang Lijun
,
Zhao Chang
, and
Zhang Mingxu

Abstract

The sensitivity of the ill-conditioned coefficient matrix (CM) and the size of the analysis volume on the retrieval accuracy in the volume velocity processing (VVP) method are analyzed. By estimating the upper limit of the retrieval error and analyzing the effects of neglected parameters on retrieval accuracy, the simplified wind model is found to decrease the difficulty in solving and stabilizing the retrieval results, even though model errors would be induced by neglecting partial parameters. Strong linear correlation among CM vectors would cause an ill-conditioned matrix when more parameters are selected. By using exact coordinate data and changing the size of the analysis volume, the variation of the condition number indicates that a large volume size decreases the condition number, and the decrease caused by increasing the number of volume gates is larger than that caused by increasing the sector width. Using the spread of errors in the solution, a demonstration using mathematical deduction is provided to explain how a large analysis volume can improve retrieval accuracy. A test with a uniform wind field is used to demonstrate these conclusions.

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Guangzhen Jin
,
Haidong Pan
,
Qilin Zhang
,
Xianqing Lv
,
Wei Zhao
, and
Yuan Gao

Abstract

As an effective tool to distinguish different tidal components, classical tidal current harmonic analysis has been widely used to obtain harmonic parameters of internal tidal currents. However, harmonic parameters cannot exactly reveal the motion of internal tides, as the irregular temporal variations for internal tides are significant in many regions of the world’s oceans. An enhanced harmonic analysis (EHA) algorithm based on the independent point scheme and cubic spline interpolation is presented in this paper to obtain harmonic parameters with temporal variations for different tidal constituents of internal tides. Moreover, this algorithm is applied to analyze 14 months of current data obtained from a mooring located on the continental shelf in the northeastern region of the South China Sea. The obvious irregular temporal variations for the four principal constituents—M2, K1, S2, and O1—of internal tides in this region are indicated. It is hoped that this algorithm might present a brand-new view for researchers to investigate the irregular temporal motions of internal tides.

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Hui Sun
,
Wei Zhao
,
Qingxuan Yang
,
Shuqun Cai
,
Xinfeng Liang
, and
Jiwei Tian

Abstract

Internal waves can transfer energy from large-scale to microscale processes; however, the spectra of these waves remain poorly known. A method that combines modal harmonic decomposition and maximum-likelihood method is proposed in this study to estimate four-dimensional internal wave spectrum using limited mooring observations. Using this method, a four-dimensional internal wave spectrum was obtained for the first time based on the mooring measurements collected during the South China Sea (SCS) Internal Wave Experiment in July 2014. The spectrum was then validated by comparing with the spectrum based on Fourier analysis and with the modified Garrett–Munk internal wave spectrum, respectively. The power of the internal wave spectrum decreased obviously with increasing frequency and wavenumber, with a falloff rate of ω −2 beyond tidal frequencies, and with falloff rates of k h 2 and k z 2.5 for horizontal and vertical wavenumbers, respectively. In addition, at a fixed frequency and vertical wavenumber, the propagation direction and phase speed of internal waves can be obtained through the four-dimensional spectrum. In summary, we verified the feasibility of estimating four-dimensional internal wave spectrum using limited mooring observations in this study, and the method we proposed should be applicable to other regions where such mooring observations are available.

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Yuxin Zhao
,
Dequan Yang
,
Wei Li
,
Chang Liu
,
Xiong Deng
,
Rixu Hao
, and
Zhongjie He

Abstract

A spatiotemporal empirical orthogonal function (STEOF) forecast method is proposed and used in medium- to long-term sea surface height anomaly (SSHA) forecast. This method embeds temporal information in empirical orthogonal function spatial patterns, effectively capturing the evolving spatial distribution of variables and avoiding the typical rapid accumulation of forecast errors. The forecast experiments are carried out for SSHA in the South China Sea to evaluate the proposed model. Experimental results demonstrate that the STEOF forecast method consistently outperforms the autoregressive integrated moving average (ARIMA), optimal climatic normal (OCN), and persistence prediction. The model accurately forecasts the intensity and location of ocean eddies, indicating its great potential for practical applications in medium- to long-term ocean forecasts.

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Zepei Wu
,
Shuo Liu
,
Delong Zhao
,
Ling Yang
,
Zixin Xu
,
Zhipeng Yang
,
Dantong Liu
,
Tao Liu
,
Yan Ding
,
Wei Zhou
,
Hui He
,
Mengyu Huang
,
Ruijie Li
, and
Deping Ding

Abstract

Cloud particles have different shapes in the atmosphere. Research on cloud particle shapes plays an important role in analyzing the growth of ice crystals and the cloud microphysics. To achieve an accurate and efficient classification algorithm on ice crystal images, this study uses image-based morphological processing and principal component analysis to extract features of images and apply intelligent classification algorithms for the Cloud Particle Imager (CPI). Currently, there are mainly two types of ice-crystal classification methods: one is the mode parameterization scheme, and the other is the artificial intelligence model. Combined with data feature extraction, the dataset was tested on 10 types of classifiers, and the highest average accuracy was 99.07%. The fastest processing speed of the real-time data processing test was 2000 images per second. In actual application, the algorithm should consider the processing speed, because the images are on the order of millions. Therefore, a support vector machine (SVM) classifier was used in this study. The SVM-based optimization algorithm can classify ice crystals into nine classes with an average accuracy of 95%, blurred frame accuracy of 100%, with a processing speed of 2000 images per second. This method has a relatively high accuracy and faster classification processing speed than the classic neural network model. The new method could be also applied in physical parameter analysis of cloud microphysics.

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