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Abstract The summer Eurasian westerly jet is reported to become weaker and wavier, thus promoting the frequent weather extremes. However, the primary driver of the changing jet stream remains in debate, mainly due to the regionality and seasonality of the Eurasian jet. Here we report a sharp increase, by approximately 140%, in the interannual variability of the summertime East Asian jet (EAJ) since the end of twentieth century. Such interdecadal change induces considerable changes in the large-scale circulation pattern across Eurasia, and consequently weather and climate extremes including heatwaves, droughts, and Asian monsoonal rainfall regime shifts. The trigger mainly emerges from preceding February North Atlantic seesaw called Scandinavian pattern (contributing to 81.1 ± 2.9% of the enhanced EAJ variability), which harnesses the “cross-seasonal-coupled oceanic-atmospheric bridge” to exert a delayed impact on EAJ and thus aids relevant predictions five months in advance. However, projections from state-of-the-art models with prescribed anthropogenic forcing exhibit no similar circulation changes. This sheds light on that, at the interannual timescale, a substantial portion of recently increasing variability in the East Asian sector of the Eurasian westerly jet arises from unforced natural variability.

Abstract Based on the temperature data recorded by a mooring system deployed in the northern South China Sea (SCS), this study compares the traditional methods of identifying and extracting internal solitary waves (ISWs): the whole water column isotherm (WCI) method and the single isotherm (SI) method, and proposes a novel method: the temperature superposition (TS) method. Results indicate that the TS method identifies the most ISWs, followed by the WCI method, and the SI method identifies the fewest. When the ISW amplitudes are smaller than 60 m, the TS method can identify the most ISWs. When the ISW amplitudes are greater than 60 m, the number of ISWs identified by the three methods is the same. The TS method removes the internal tides, takes into account the temperature of all depth layers, and amplifies the signal of ISWs to a certain extent, so as to effectively identify ISWs.

Abstract Based on the velocity and temperature data recorded by two acoustic Doppler current profilers (ADCPs) at a mooring system deployed in the northern South China Sea (SCS), this study investigates the characteristics of near-inertial waves (NIWs) induced by typhoons Bebinca, Barijat, Mangkhut and Yutu in 2018. For the dynamical response, besides the motion of near inertial frequency induced by typhoons, the motion of 2 f ([1.80–2.20] f, f is the local inertial frequency) and f D1 (a harmonic wave with a frequency equal to the sum of frequencies of NIWs and diurnal tides) frequency will also increase. For near-inertial motions, the maximum near-inertial kinetic energy (NIKE) is confined to depths above 150 m. For stronger (weaker) wind forcing, the longer (shorter) the response time of the ocean to the atmospheric forcing is, and the shorter (longer) the response time is required in relaxation stage. There are upward and downward propagating energies after the passage of typhoons, and the upward propagating energy mainly occur in the stage of the geostrophic balance adjustment. The current structure suggests that the NIWs in the vertical direction are two antisymmetric rotary vortices in a near-inertial period, which is similar to the structure of the Langmuir circulation. Besides, the horizontal near-inertial currents (NICs) are much stronger than the vertical NICs, and the stronger the NIWs are, the stronger the horizontal NICs relative to the vertical NICs are. For the temperature response, the temperature variation reflects a clear stratified vertical structure. In the forcing stage, the upper layer becomes colder, the lower layer becomes warmer, and the thickness and intensity of the thermocline decrease. In the relaxation stage, the upper layer warms and the lower layer cools, and the thickness and intensity of thermocline increase.

Speech Emotion Recognition (SER) plays an important role in the field of Human-Computer Interaction (HCI). With the rapid development of machine learning technology, SER based on deep learning has also made new progress. At the same time, the insufficient performance of single feature leads to a lower recognition accuracy. Aiming at this problem, in this study, a multi-feature fusion method is proposed to improve the accuracy of SER. Firstly, several different spectrogram features are divided into three sub-spectrograms along frequency to make the local features more prominent. Secondly, each independent sub-spectrogram is used as input of the improved Residual Network (ResNet) structure based on attention mechanism to extract emotion feature maps. Finally, the emotion feature maps are fused in the feature layer which is connected to the output layer. The experimental results show that the mentioned method can better classify kinds of emotions. Specifically, an accuracy of 98% is achieved on the ESD dataset, which is nearly 6 percentage points higher than other methods. In order to verify the effectiveness of the method, we also carried out experiments on the SER standard dataset IEMOCAP, and the accuracy rate reached 76.54%. Proving the effectiveness of the mentioned method.

Internal solitary wave (ISW) is one of the most important sub-mesoscale phenomenas in the ocean, and detection of which is of utmost significance for marine exploitation, ecological environment protection and military affairs. Therefore, in this study, an automatic algorithm for the identification of ISWs was proposed based on the geometric characteristics of the flow field. The algorithm was applied to calculate the characteristic parameters of the ISWs and its reliability was further verified, indicating that the algorithm can effectively detect the ISWs and provide a reference for preventing and avoiding ISWs in ocean engineering.

The bottom current is an important component of the three-dimensional ocean circulation, which is of significance for the safeties of ocean bottom engineering and facilities, the research on sediment and pollutant transports, and the ecological environment. Due to the lack of observation data, research on the bottom current in the South China Sea (SCS) has been limited. This study systematically analyzes bottom currents from 0.5 to 5 m above the seafloor based on 20-month-long mooring observations on the shelf slope west of the Dongsha Atoll. The spectral analysis results indicate that currents induced by the internal tides and the internal solitary waves (ISWs) comprise dominant constituents of bottom currents with velocity amplitudes up to O(50–90) cm/s. Dominant internal tide-induced bottom currents of the velocity amplitude of O(50) cm/s occupy 53% of the total horizontal kinetic energy. The pulsating ISW-induced bottom currents reach a maximum amplitude of 93 cm/s, which has intrinsic relation to the amplitude of the ISWs, according to the soliton propagation speed solutions of the Korteweg–De Vries (KdV) equation. The background bottom current is strongest in winter, followed by spring, and weak in summer and autumn, which is closely correlated with the behavior of mesoscale eddies. These results are of significance for understanding the dynamics of the bottom boundary layer.

Adding the auxiliary frequency control function to the wind turbine generator (WTG) is a solution to the frequency security problem of the power system caused by the replacement of the synchronous generator (SG) by the WTG. The auxiliary frequency control using rotor kinetic energy is an economical scheme because the WTG still runs at the maximum power point during normal operation. In this paper, the functional optimization model of the auxiliary frequency control strategy of WTG is established. The optimal auxiliary frequency control strategy is obtained by solving the model numerically. As for the practical realization of the control strategy, the coordination of the auxiliary frequency control with the maximum power point tracking (MPPT) control is studied. The practical auxiliary frequency control strategy is modified to adapt to different power disturbances in the system, and the parameter setting method is also proposed. The sensitivity of system frequency to control parameters is studied. Finally, the simulation results verify the effectiveness and practicability of the proposed control strategy.

Outlier detection is the key to the quality control of marine survey data. For the detection of outliers in Conductivity-Temperature-Depth (CTD) data, previous methods, such as the Wild Edit method and the Median Filter Combined with Maximum Deviation method, mostly set a threshold based on statistics. Values greater than the threshold are treated as outliers, but there is no clear specification for the selection of threshold, thus multiple attempts are required. The process is time-consuming and inefficient, and the results have high false negative and positive rates. In response to this problem, we proposed an outlier detection method in CTD conductivity data, based on a physical constraint, the continuity of seawater. The method constructs a cubic spline fitting function based on the independent points scheme and the cubic spline interpolation to fit the conductivity data. The maximum fitting residual points will be flagged as outliers. The fitting stops when the optimal number of iterations is reached, which is automatically obtained by the minimum value of the sequence of maximum fitting residuals. Verification of the accuracy and stability of the method by means of examples proves that it has a lower false negative rate (17.88%) and false positive rate (0.24%) than other methods. Indeed, rates for the Wild Edit method are 56.96% and 2.19%, while for the Median Filter Combined with Maximum Deviation method rates are 23.28% and 0.31%. The Cubic Spline Fitting method is simple to operate, the result is clear and definite, better solved the problem of conductivity outliers detection.

Due to the regional differences between the North and South Yellow Sea, and under the influence of winter winds, the relative changes in the coastal current and the Yellow Sea warm current will lead to the instability of the front, which will lead to the cross-front transport of sediment. Therefore, the study of sediment exchange between the North and South Yellow Sea has become an indispensable part of the study of the Yellow Sea environment. In this study, the current field and sediment concentration in the southern part of Chengshantou, a representative area of the Yellow Sea, were observed in winter in order to analyze the sediment exchange process between the North Yellow Sea and the South Yellow Sea in winter. The observation results show that in the southern sea area of Chengshantou, in winter, the current velocity does not change with the water depth when it exceeds 15 m, and the tides are regular semi-diurnal tides. When the water depth is less than 15 m, the current direction changes clockwise with the increase in the water depth. The turbidity increases rapidly when the wind direction is offshore and the bottom residual current is onshore, which may cause the sediment transported offshore under the action of wind and ocean current to settle under the obstruction of the Yellow Sea warm current, resulting in the rise of bottom turbidity. This also indicates that the change in residual current direction at different water depths may also lead to an increase in suspended sediment concentration. Based on this, in the estuarine area, the relative change in the current direction between the wind current and the coastal current may also be the cause of the change in the maximum turbidity zone.

Comprehensive understanding of the spatial characteristics of tropical cyclone (TC) precipitation is essential for effective socioeconomic planning and scientific research. The present study examines the spatial asymmetry in TC precipitation over the western North Pacific (WNP) concerning various factors, including latitudes, sea surface temperature (SST), TC intensity, and translation speed, based on satellite observations. The results reveal a significant poleward migration of TC precipitation asymmetry particularly above 15° N. Furthermore, the asymmetry exhibits considerable sensitivity to changes in TC center latitudes and SSTs, characterized by anticlockwise and northeastward migration, respectively. The poleward migration of TC precipitation spatial asymmetry is primarily due to the poleward decreasing SST, increasing vertical wind shear and increasing TC translation speed. These findings contribute to a comprehensive understanding of TC behavior over the WNP and provide valuable insights for disaster preparedness and mitigation efforts.

With the rapid development of remote-sensing imaging technology, remote-sensing images have become increasingly diverse, and people are paying more attention to ocean remote-sensing research. Because ocean remote-sensing data are complex, and the ocean environment is diverse, results will differ, even if the same target is detected at different times in the same scene. To obtain more semantic features and better pixel-level prediction capabilities, this paper proposes a pixel-level ocean remote-sensing image algorithm (GLPO-Net) that combines local and global features. First, texture features, color features, and spatial relationship features are extracted. Second, the algorithm constructs a multiscale local cross-attention mechanism strategy to obtain feature weight information in different directions to fully mine the local features of ocean remote-sensing images. Concurrently, an algorithm constructs a multiscale global cross-attention mechanism strategy to obtain global features. Then, the fusion of global features and local features is described in each submodule to obtain more representative deep features. Finally, small-sample ocean remote-sensing is described via image pixel-level prediction. The algorithm proposed in this paper has been tested with three public ocean remote-sensing datasets. The experimental results show that the proposed GLPO-Net algorithm can learn features from small samples of ocean remote-sensing images. Compared to the prediction results of other remote-sensing image algorithms, GLPO-Net exhibits better prediction capabilities.

With the development of remote sensing technology, the semantic segmentation and recognition of various things in the ocean have become more and more frequent. Due to the wide variety of marine things and the large differences in morphology, it has brought greater difficulties to the recognition of marine remote sensing images. In order to obtain better segmentation results of ocean remote sensing images, this paper proposes an cross attention mechanism(Horizontal and Vertical) of exponential operation combined with multi-scale convolution algorithm. Among them, the cross attention mechanism and expanded distribution weight coefficient mentioned in this paper are first proposed. First, Input the marine remote sensing image features into an cross attention mechanism algorithm of exponential operation to obtain feature weight coefficients and joint weight coefficients in multiple directions; Then, the features with weight coefficients are input into the multi-access convolutional layer and the multi-scale dilated convolutional layer respectively for deep feature mining; Then the above steps are repeated twice, and finally the semantic segmentation of marine remote sensing images is achieved by fusing multiple deep-level features afterwards. Experiments were conducted on three public marine remote sensing data sets, and the results proved the effectiveness of our proposed cross attention mechanism of extended operation algorithm. The F values of the MAMC model on Beach, Island and Sea ice data sets have reached 99.4%, 91.25%, 87.08% respectively. Compared with other models, the effect is significantly improved, and proved the powerful performance of the algorithm in the semantic segmentation of marine remote sensing images.

In the field of Human-Computer Interaction (HCI), speech emotion recognition technology plays an important role. Facing a small number of speech emotion data, a novel speech emotion recognition method based on feature construction and ensemble learning is proposed in this paper. Firstly, the acoustic features are extracted from the speech signal and combined to form different original feature sets. Secondly, based on Light Gradient Boosting Machine (LightGBM) and Sequential Forward Selection (SFS) method, a novel feature selection method named L-SFS is proposed. And then, the softmax regression model is used to learn automatically the weights of the four single weak learners including Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Extreme Gradient Boosting (XGBoost) and LightGBM. Lastly, based on the learned automatically weights and the weighted average probability voting strategy, an ensemble classification model named Sklex is constructed, which integrates the above four single weak learners. In conclusion, the method reflects the effectiveness of feature construction and the superiority and stability of ensemble learning, and gets good speech emotion recognition accuracy.

An integrated interferometric fiber optic velocimetry sensor has been proposed and demonstrated at the central wavelength of 638 nm. The sensor is based on the principle of two laser-beams’ interference. The light signal scattered from the particles or vapor is demodulated to measure the water surface velocity and water vapor velocity. Three velocity measurement experiments are carried out to measure the velocity, and the experimental data shows that the velocity increases linearly in the range of 4 mm·s−1 to 100 mm·s−1, with a slope of linear fitting curve of 0.99777 and the R-Square of 1.00000. The velocity calculated from frequency shift fits well with the reference velocity. The maximum average relative error in the three velocity measurements is less than 2.5%. In addition, the maximum speed of 4.398 m·s−1 is confirmed in the rotating disk calibration experiment, which expands the sensor’s velocity measurement range. To solve the problem that it is difficult to directly measure the velocity of small-scale water surface flow velocity, especially from the aspect of the low velocity of air-water surface, the interferometric fiber optic sensor can be applied to the measurement of water surface velocity and wind velocity on the water surface.

We investigated the early-stage development of cyclones occurring in the strong baroclinic regions in the Northwest Pacific and the Northwest Atlantic based on European Center for Medium-range Weather Forecasts Re-Analysis-Interim (ERA-Interim) data. The composite background conditions corresponding to the cyclones on the onset day are characterized by upper troposphere divergence of westerly jet ahead of a trough, low troposphere convergence of westerly jet behind a trough, and strong meridional air temperature gradient (baroclinicity) both in the Northwest Pacific and the Northwest Atlantic, but with stronger baroclinicity in the Northwest Pacific. The composite velocity and temperature fields of the cyclone on the onset day show a clear horizontal front and a westward and northward vertical tilting of cyclonic circulation to the cold zone. The composite Northwest Pacific cyclone filed on the onset day has a warm core, whereas the composite Northwest Atlantic cyclone field has a cold core in the low troposphere. The leading adiabatic processes that contribute to the developing of the cold core cyclone in the Northwest Atlantic on the onset day is the temperature advection, while stronger vertical motion induces stronger adiabatic warming in the Northwest Pacific cyclones, which has a significant contribution to the development of warm core cyclones on the onset day.