An Investigation of the Effects of Wave State and Sea Spray on an Idealized Typhoon Using an Air-Sea Coupled Modeling System

In this study, the impact of atmospherewave coupling on typhoon intensity was investigated using numerical simulations of an idealized typhoon in a coupled atmospherewaveocean modeling system. The coupling between atmosphere and sea surface waves considered the effects of wave state and sea sprays on airsea momentum flux, the atmospheric lowlevel dissipative heating, and the wavestateaffected sea spray heat flux. Several experiments were conducted to examine the impacts of wave state, sea sprays, and dissipative heating on an idealized typhoon system. Results show that considering the wave state and seasprayaffected seasurface roughness reduces typhoon intensity, while including dissipative heating intensifies the typhoon system. Taking into account sea spray heat flux also strengthens the typhoon system with increasing maximum wind speed and significant wave height. The overall impact of atmospherewave coupling makes a positive contribution to the intensification of the idealized typhoon system. The minimum central pressure simulated by the coupled atmospherewave experiment was 16.4 hPa deeper than that of the control run, and the maximum wind speed and significant wave height increased by 31% and 4%, respectively. Meanwhile, within the area beneath the typhoon center, the average total upward airsea heat flux increased by 22%, and the averaged latent heat flux increased more significantly by 31% compared to the uncoupled run.

Nonparametric estimations of the sea state bias for a radar altimeter

To estimate the sea state bias（SSB） for radar altimeter, two nonparametric models, including a Nadaraya-Watson（NW） kernel estimator and a local linear regression（LLR） estimator, are studied based on the Jason-2 altimeter data. Selecting from different combinations of the Gaussian kernel function, spherical Epanechnikov kernel function, a fixed bandwidth and a local adjustable bandwidth, it is observed that the LLR method with the spherical Epanechnikov kernel function and the local adjustable bandwidth is the optimal nonparametric model for the SSB estimation. The comparisons between the nonparametric and parametric models are conducted and the results show that the nonparametric model performs relatively better at high-latitudes of the Northern Hemisphere. This method has been applied to the HY-2A altimeter as well and the same conclusion can be obtained.

Optimal Stiffness for Flexible Connectors on A Mobile Offshore Base at Rough Sea States

This paper investigates a simplified method to determine the optimal stiffness of flexible connectors on a mobile offshore base(MOB) during the preliminary design stage. A three-module numerical model of an MOB was used as a case study. Numerous constraint forces and relative displacements for the connectors at rough sea states with different wave angles were utilized to determine the optimized stiffness of the flexible connectors. The range of optimal stiffnesses for the connectors was obtained based on the combination and intersection of the optimized stiffness results, and the implementation steps were elaborated in detail. The percentage reductions of the optimized and optimal stiffness of the flexible connector were determined to quantitatively evaluate the decreases of the constraint force and relative displacement of the connectors compared with those calculated by using the original range of the connector stiffnesses. The results indicate the accuracy and feasibility of this method for determining the optimal stiffness of the flexible connectors and demonstrate the rationality and practicability of the optimal stiffness results. The research ideas, calculation process, and solutions for the optimal stiffness of the flexible connectors of an MOB in this paper can provide valuable technical support for the design of the connectors in similar semisubmersible floating structures.

Trace-Norm Regularized Multi-Task Learning for Sea State Bias Estimation

Sea state bias(SSB)is an important component of errors for the radar altimeter measurements of sea surface height(SSH).However,existing SSB estimation methods are almost all based on single-task learning(STL),where one model is built on the data from only one radar altimeter.In this paper,taking account of the data from multiple radar altimeters available,we introduced a multi-task learning method,called trace-norm regularized multi-task learning(TNR-MTL),for SSB estimation.Corresponding to each individual task,TNR-MLT involves only three parameters.Hence,it is easy to implement.More importantly,the convergence of TNR-MLT is theoretically guaranteed.Compared with the commonly used STL models,TNR-MTL can effectively utilize the shared information between data from multiple altimeters.During the training of TNR-MTL,we used the JASON-2 and JASON-3 cycle data to solve two correlated SSB estimation tasks.Then the optimal model was selected to estimate SSB on the JASON-2 and the HY-270-71 cycle intersection data.For the JSAON-2 cycle intersection data,the corrected variance(M)has been reduced by 0.60 cm^2 compared to the geophysical data records(GDR);while for the HY-2 cycle intersection data,M has been reduced by 1.30 cm^2 compared to GDR.Therefore,TNR-MTL is proved to be effective for the SSB estimation tasks.

The Influence of Wave State and Sea Spray on Drag Coefficient from Low to High Wind Speeds

Ocean waves alter the roughness of sea surface,and sea spray droplets redistribute the momentum flux at the air-sea interface.Hence,both wave state and sea spray influence sea surface drag coefficient.Based on the new sea spray generation function which depends on sea surface wave,a wave-dependent sea spray stress is obtained.According to the relationship between sea spray stress and the total wind stress on the sea surface,a new formula of drag coefficient at high wind speed is acquired.With the analysis of the new drag coefficient,it is shown that the drag coefficient reduces at high wind speed,indicating that the sea spray droplets can limit the increase of drag coefficient.However,the value of high wind speed corresponding to the initial reduced drag coefficient is not fixed,and it depends on the wave state,which means the influence of wave cannot be ignored.Comparisons between the theoretical and measured sea surface drag coefficients in field and laboratory show that under different wave ages,the theoretical result of drag coefficient could include the measured data,and it means that the new drag coefficient can be used properly from low to high wind speeds under any wave state condition.

Sea State Bias Estimation with Least Absolute Shrinkage and Selection Operator(LASSO)

With the sea-level rising,the measurement of sea surface height(SSH) is attracting more and more attention in the area of oceanography.Satellite radar altimeter is usually used to measure the SSH.However,deviation between the measured value and the actual one always exists.Among others,the sea state bias(SSB) is a main reason to cause the deviation.In general,one needs to estimate SSB first to correct the measured SSH.Currently,existing SSB estimation methods more or less have shortcomings,such as with many parameters,high prediction error and long training time.In this paper,we introduce an effective and efficient linear model called LASSO to the SSB estimation.The LASSO algorithm minimizes the residual sum of squares under the condition that the sum of the absolute values of each coefficient is less than a certain constant.In the implementation of LASSO,we use the significant wave height and wind speed to fit the LASSO model.Hence,the applied model has only 3 parameters,corresponding to the two inputs and a bias.Experimental results on the data of JASON-2,JASON-3,T/P and HY-2 radar altimetry show that LASSO performs better than geophysical data records(GDR) and ordinary least squares(OLS) estimator.Moreover,from the running time,we can see that LASSO is very efficient.

Estimate of Global Sea-Air CO_2 Flux with Sea-State-Dependent Parameterization

Although the annual global sea-air CO2 flux has been estimated extensively with various wind-dependent-k parameterizations,uncertainty still exists in the estimates. The sea-state-dependent-k parameterization is expected to improve the uncertainty existing in these estimates. In the present study,the annual global sea-air CO2 flux is estimated with the sea-state-dependent-k parameterization proposed by Woolf(2005) ,using NOAA/NCEP reanalysis wind speed and hindcast wave data from 1998 to 2006,and a new estimate,-2.18 Gt C year-1,is obtained,which is comparable with previous estimates with biochemical methods. It is interesting to note that the averaged value of previous estimates with various wind-dependent-k parameterizations is almost identical to that of previous estimates with biochemical methods by various authors,and that the new estimate is quite consistent with these averaged estimates.

THE DEPENDENCE OF WIND STRESS ON SEA STATE

Based on up to date literature, this paper details the evolution of wave dependence of wind stress.Some typical models of the dependence of wind stress on waves are described in detail. Although there isno universally accepted theory and model, recent studies indicate that the wind strees strongly dependson the development state of sea waves, i. e., young seas are rougher than mature seas, in other words, thewind stress decreases with increasing wave age.

INTERDECADAL SEA LEVEL VARIATION AT THE JAPANESE COAST AND LARGE SCALE CLIMATE STATE

Thirty years of monthly mean anomalies of sea level(SL) at 15 Japanese coastal stations, sea sur-face temperature (SST) and sea level pressure (SLP) in or over the northern Pacific were analyzed bycanonical correlation analysis (CCA) to study the relationship between the interdecadal SL variationand large scale climate state. Given two time-varying fields this technique identifies the pair ofspacial patterns with optimally correlated time series.The results show that there are two important air-sea interactive processes in the extratropicalPacific region for the variation of the SL at the Japanese coast on interdecadal scale. One is theocean heating or cooling of the atmosphere over the Kuroshio extension region, which results in ahuge SLP anomalous vortex with planetary spacial scale big enough to change the global climate. An-other is the large Kuroshio meander phenomenon controlled by the large-scale wind-stress curls oneyear earlier in the adjacent region of the Hawaiian Islands. The first process

Effect of Decadal Changes in Air-Sea Interaction on the Climate Mean State over the Tropical Pacific

Collaboration of interannual variabilities and the climate mean state determines the type of El Nio. Recent studies highlight the impact of a La Nia-like mean state change, which acts to suppress the convection and low-level convergence over the central Pacific, on the predominance of central Pacific(CP) El Nio in the most recent decade. However, how interannual variabilities affect the climate mean state has been less thoroughly investigated. Using a linear shallow-water model, the effect of decadal changes of air-sea interaction on the two types of El Nio and the climate mean state over the tropical Pacific is examined. It is demonstrated that the predominance of the eastern Pacific(EP) and CP El Nio is dominated mainly by relationships between anomalous wind stresses and sea surface temperature(SST). Furthermore, changes between air-sea interactions from 1980–98 to 1999–2011 prompted the generation of the La Nialike pattern, which is similar to the background change in the most recent decade.

Wave Period Distributions in Non-Gaussian Mixed Sea States

The wave period probability densities in non-Gaussian mixed sea states are calculated by utilizing a transformed Gaussian process method. The transformation relating the non-Gaussian process and the original Gaussian process is obtained based on the equivalence of the level up-crossing rates of the two processes. A saddle point approximation procedure is applied for calculating the level up-crossing rates in this study. The accuracy and efficiency of the transformed Gaussian process method are validated by comparing the results predicted by using the method with those predicted by the Monte Carlo simulation method.

Estimating the evolution of sea state non-Gaussianity based on a phase-resolving model

The occurrence of rogue waves is closely related to the non-Gaussianity of sea states,and this non-Gaussianity can be estimated using corresponding two-dimensional wave spectra.This paper presents an approach to non-Gaussianity estimation based on a phase-resolving model called the high-order spectral method(HOSM).Based on numerous HOSM simulations,a set of precalculated non-Gaussianity indicators was established that could be applied to real sea states without any calibration of spectral shapes.With a newly developed extraction approach,the indicators for given two-dimensional wave spectra could then be conveniently extracted from the precalculated dataset.The feasibility of the newly developed approach in a real wave environment is verified.Using the estimation approach,phase-resolved non-Gaussianity can now be illustrated throughout the evolution of sea states of interest,not just at a few specific times;and the level of non-Gaussianity at any time in a duration can be identified according to the statistics(e.g.,quantities)of the phase-resolved indicators,that are obtained throughout the duration concerned.

An Improved Method of Retrieving Sea Surface Wind Speed Based on a Four-Layer Medium Model at High Sea States

Considering about the effect of whitecaps and foams on pulse-limited Radar Altimeters, an improved algorithm of retrieving sea surface wind speed is proposed in this paper. Firstly, a four-layer dielectric model is established in order to simulate an air-sea interface. Secondly, the microwave reflectivity of a sea surface covered by spray droplets and foams at 13.5 GHz is computed based on the established model. These computed results show that the effect of spray droplets and foams in high sea state conditions shall not be negligible on retrieving sea surface wind speed. Finally, compared with the analytical algorithms proposed by Zhao and some calculated results based on a three-layer dielectric model, an improved algorithm of retrieving sea surface wind speed is presented. At a high wind speed, the improved algorithm is in a better accord with some empirical algorithms such as Brown, Young ones and et al., and also in a good agreement with ZT and other algorithms at low wind speed. This new improved algorithm will be suitable not only for low wind speed retrieval, but also for high wind speed retrieval. Better accuracy and effectiveness of wind speed retrieval can also be obtained.

Device-Free Through-the-Wall Activity Recognition Using Bi-Directional Long Short-Term Memory and WiFi Channel State Information

Activity recognition plays a key role in health management and security.Traditional approaches are based on vision or wearables,which only work under the line of sight(LOS)or require the targets to carry dedicated devices.As human bodies and their movements have influences on WiFi propagation,this paper proposes the recognition of human activities by analyzing the channel state information(CSI)from the WiFi physical layer.The method requires only the commodity:WiFi transmitters and receivers that can operate through a wall,under LOS and non-line of sight(NLOS),while the targets are not required to carry dedicated devices.After collecting CSI,the discrete wavelet transform is applied to reduce the noise,followed by outlier detection based on the local outlier factor to extract the activity segment.Activity recognition is fulfilled by using the bi-directional long short-term memory that takes the sequential features into consideration.Experiments in through-the-wall environments achieve recognition accuracy>95%for six common activities,such as standing up,squatting down,walking,running,jumping,and falling,outperforming existing work in this field.

Short-Term and Long-Term Price Forecasting Models for the Future Exchange of Mongolian Natural Sea Buckthorn Market

Sea buckthorn market floated uncertainly within a narrow range. The market situation provided upward pressure on prices, and producer and consumer interest were poor, coupled with weak prices in the regional markets. The objectives of the study are: 1) to estimate the relationship between wild Sea buckthorn (SB) price and Supply, Demand, while some other factors of crude oil price and exchange rate by using simultaneous Supply-Demand and Price system equation and Vector Error Correction Method (VECM);2) to forecast the short-term and long-term SB price;3) to compare and evaluate the price forecasting models. Firstly, the data was analyzed by Ferris and Engle-Granger’s procedure;secondly, both price forecasting methodologies were tested by Pindyck-Rubinfeld and Makridakis’s procedure. The result shows that the VECM model is more efficient using yearly data;a short-term price forecast decreases, and a long-term price forecast is predicted to increase the Mongolian Sea buckthorn market.

Statistical Analyses of Sea State Conditions in South China Sea

The statistical characterization of sea conditions in the South China Sea(SCS) was investigated by analyzing a 30-year(1976–2005) numerically simulated daily wave height and wind speed data. The monthly variation of these parameters shows that wave height and wind speed have minimum values of 0.54 m and 4.15 ms^(-1), respectively in May and peak values of 2.04 m and 8.12 ms^(-1), respectively in December. Statistical analysis of the daily wave height and wind speed and the subsequent characterization of the annual, seasonal and monthly mean sea state based on these parameters were also done. Results showed that, in general, the slight sea state prevails in the SCS and has nearly the highest occurrence in all seasons and months. The moderate sea condition prevails in the winter months of December and January while the smooth(wavelets) sea state prevails in May. Furthermore, spatial variation of sea states showed that calm and smooth sea conditions have high occurrences(25%–80%) in the southern SCS. The slight sea condition shows the largest occurrence(25%–55%) over most parts of the SCS. High occurrences(8%–17%) of the rough and very rough seas distribute over some regions in the central SCS. Sea states from high to phenomenal conditions show rare occurrence(<12%) in the northern SCS. The calm(glassy) sea condition shows no occurrence in the SCS.

State of Health Estimation of Lithium-Ion Batteries Using Support Vector Regression and Long Short-Term Memory

Lithium-ion batteries are the most widely accepted type of battery in the electric vehicle industry because of some of their positive inherent characteristics. However, the safety problems associated with inaccurate estimation and prediction of the state of health of these batteries have attracted wide attention due to the adverse negative effect on vehicle safety. In this paper, both machine and deep learning models were used to estimate the state of health of lithium-ion batteries. The paper introduces the definition of battery health status and its importance in the electric vehicle industry. Based on the data preprocessing and visualization analysis, three features related to actual battery capacity degradation are extracted from the data. Two learning models, SVR and LSTM were employed for the state of health estimation and their respective results are compared in this paper. The mean square error and coefficient of determination were the two metrics for the performance evaluation of the models. The experimental results indicate that both models have high estimation results. However, the metrics indicated that the SVR was the overall best model.

Ecological evaluation of marine macroalgal communities on five islands of Korea in the Yellow Sea

Macroalgae have long been used as biological indicators of marine ecosystem health worldwide due to their ecological importance and sensitivity to environmental stress.A number of previous studies have utilized macroalgal communities in monitoring surveys of environmental conditions.This study examined the characteristics and patterns of marine macroalgal communities in the Yellow Sea off the western coast of Korea.Macroalgae were analyzed for the number of species,biomass,and coverage ratio by macroalgal type.During the study period,82 macroalgal species(10 green algae,17 brown algae,and 55 red algae)were identified at the five study sites,with the highest number of species found at Gwanrido and Uido(both containing 41 species)and the lowest at Daeijakdo(27 species).The average biomass(via dry weight)was 98.63 g/m^(2),consisting of green algae(8.39 g/m^(2)),brown algae(35.08 g/m^(2)),and red algae(55.16 g/m^(2)).The dominant macroalgae species in terms of biomass were Corallina pilulifera,Sargassum thunbergii,and Ulva australis in the intertidal zones,and Botryocladia wrightii and Gelidium elegans in the subtidal zones.Richness,evenness,and diversity indices based on the biomass of abundant species were 5.08,0.65,and 2.30,respectively,over the entire study area.Based on the evaluation of the environmental states by the community indices,overall,the Ecological Evaluation Index of macroalgae communities in the study area was marked as“Good-Moderate”,but was determined as“ModerateLow”at several sites during summer.The results can be a direct approach in the assessment of coastal habitats in which anthropogenic as well as climate change influences persist.

Neural Network-Based State of Charge Estimation Method for Lithium-ion Batteries Based on Temperature

Lithium-ion batteries are commonly used in electric vehicles,mobile phones,and laptops.These batteries demonstrate several advantages,such as environmental friendliness,high energy density,and long life.However,battery overcharging and overdischarging may occur if the batteries are not monitored continuously.Overcharging causesfire and explosion casualties,and overdischar-ging causes a reduction in the battery capacity and life.In addition,the internal resistance of such batteries varies depending on their external temperature,elec-trolyte,cathode material,and other factors;the capacity of the batteries decreases with temperature.In this study,we develop a method for estimating the state of charge(SOC)using a neural network model that is best suited to the external tem-perature of such batteries based on their characteristics.During our simulation,we acquired data at temperatures of 25°C,30°C,35°C,and 40°C.Based on the tem-perature parameters,the voltage,current,and time parameters were obtained,and six cycles of the parameters based on the temperature were used for the experi-ment.Experimental data to verify the proposed method were obtained through a discharge experiment conducted using a vehicle driving simulator.The experi-mental data were provided as inputs to three types of neural network models:mul-tilayer neural network(MNN),long short-term memory(LSTM),and gated recurrent unit(GRU).The neural network models were trained and optimized for the specific temperatures measured during the experiment,and the SOC was estimated by selecting the most suitable model for each temperature.The experimental results revealed that the mean absolute errors of the MNN,LSTM,and GRU using the proposed method were 2.17%,2.19%,and 2.15%,respec-tively,which are better than those of the conventional method(4.47%,4.60%,and 4.40%).Finally,SOC estimation based on GRU using the proposed method was found to be 2.15%,which was the most accurate.

基于EtherCAT总线的八轴七联动高级海况环境仿真平台

针对水流模拟海况时存在的运动不受控以及常规海况模拟平台模拟海况等级低,运动自由度不足等问题,提出利用八自由度运动平台模拟高级海况环境进行海浪涌动测试的方法。设计了由直线运动平台、旋转机构和6自由度(6-DOF)并联机构组成的串并联混合八自由度高级海况环境仿真平台,其运动加速度大,自由度多,运动方式多样。基于Ether CAT实时总线技术设计了八轴七联动的运动控制算法,并采用惯量匹配技术抑制抖动和噪声,实现了宽惯量范围的精确、稳定、同步(小于1μs)控制。设计了基于外部信号接收器的实时精密补偿技术和双齿轮消隙技术,提高了速率精度和平稳性,定位精度达到10μm。设计了复合叠加摇摆、文件仿真、复合叠加摇摆与文件仿真叠加运动和实时仿真运动方式,可模拟到五级高海况或将实航数据回灌后模拟水下、水面或机载的运动,用于导航系统的姿态模拟测试。