Improving the prediction of extreme FPSO hawser tension,using another highly correlated hawser tension with a longer time record

The floating production storage and offloading unit (FPSO) is an offshore vessel that produces and stores crude oil prior to tanker transport. Robust prediction of extreme hawser tensions during the FPSO offloading operation is an important safety concern. Excessive hawser tension may occur during certain sea conditions, posing an operational risk. In this paper, the finite element method (FEM) software ANSYS AQWA has been employed to analyze vessel response due to hydrodynamic wave loads, acting on a specific FPSO vessel under actual sea conditions.In some practical situations, it would be useful to improve the accuracy of some statistical predictions based on a certain stochastic random process, given another synchronous highly correlated stochastic process that has been measured for a longer time, than the process of interest. In this paper, the issue of improving extreme value prediction has been addressed. In other words, an efficient transfer of information is necessary between two synchronous, highly correlated stochastic processes. Two such highly correlated FPSO hawser tension processes were simulated in order to test the efficiency of the proposed technique.     

基于LSTM的海洋表面短期风速预测研究

为实现对海面风速精确的短期预测,提出了一种基于长短期记忆(LSTM,longshort-termmemory)神经网络的短期风速预测模型,选取OceanSITES数据库中单个浮标站点采集的风速历史数据作为模型输入,经过训练设置最佳参数等步骤,实现了以LSTM方法,对该站点所在海区海面风速在各季节性代表月份海面风速的24 h短期预测。同时通过不同预测时长的实验以及与BP(back propagation)神经网络神经网络和径向基函数神经网络(radialbasisfunctionneuralnetwork,RBF)的预测效果对比实验,证明了LSTM预测方法相比上述两种神经网络预测方法,在海表面风速预测应用中的优越性。最后通过多个海域对应的站点风速数据预测实验,证明了LSTM神经网络模型的普遍适用性,由相关系数和预测误差的分析可知该方法具备应对急剧变化数据的预测稳定性,可以作为海洋表面风速短期预测的一种可靠方法。     

A probabilistic description of the wind over Liverpool Bay with application to oil spill simulations

Surface winds from the UK Meteorological Office mesoscale (12 km grid) atmospheric model have been used to define the wind at a location in Liverpool Bay during 1997–2001. Winds from the SW (centred on 240°) with a speed of about 10 m/s (20 knots) were the most frequent, although weaker winds from the SE were also common. The wind spectra were red in character and showed no evidence for a peak at the synoptic (2–5 day) time scale; however, a zero-up-crossing analysis suggested a dominant periodicity at 3.1 days, and at this time scale the winds were spatially coherent over a distance of 300 km. A wind direction transition matrix was derived to quantify the probability with which the wind changed between two specified directions. This information was then used with an estimate of the mean duration of a wind event to compute a stochastic wind time series that contained a similar energy level, periodicity, and direction variability to the archived wind data. The archived and stochastic winds were then used in 1000 oil spill contingency simulations during which estimates of the mean and minimum times taken for oil to reach the coastline, and the percentage of the oil impacting selected sites were computed. The stochastic winds provided more realistic results, when compared against those derived using the wind archive, than those obtained using a wind rose representation of the winds. The derivation and use of a stochastic wind time series has application to a range of modelling studies.     

HY-2B卫星载荷联合观测海面阵风的一种反演方法

国内外对海上阵风的研究并不多,且大多集中在阵风预报和应用研究方面,对于海洋阵风数据的获取技术未见文献系统论述。本文利用HY-2B卫星雷达高度计观测的后向散射系数,结合校正微波辐射计观测的亮度温度信息,提出联合反演阵风风速的方法。两个遥感载荷联合反演得到的阵风风速与2019–2021年美国国家浮标数据中心（NDBC）浮标数据进行真实性检验,结果显示：阵风风速均方根误差（RMSE）为0.98 m/s,相关系数为0.82;基于本方法利用国外同类卫星Jason-3得到的阵风风速与2016–2018年NDBC浮标数据的RMSE为0.96 m/s,相关系数为0.88。本文在HY-2B卫星雷达高度计海面风速观测的基础上,纳入同一卫星平台校正微波辐射计的同步观测信息联合实现了海面阵风的观测,数据的比对结果证明文中方法具有较高的观测精度。同时,该方法对于具有相同观测体制的国内外卫星也适用。     

A note on ocean surface drift with application to surface velocities measured with HF Radar

The ocean drift current consists of a (local) pure drift current generated by the interaction of wind and waves at the sea surface, to which the surface geostrophic current is added vectorially. We present (a) a similarity solution for the wave boundary layer (which has been validated through the prediction of the 10-m drag law), from which the component of pure drift current along the direction of the wind (and hence the speed factor) can be evaluated from the 10-m wind speed and the peak wave period, and (b) a similarity solution for the Ekman layers of the two fluids, which shows that under steady-state neutral conditions the pure drift current lies along the direction of the geostrophic wind, and has a magnitude 0.034 that of the geostrophic wind speed. The co-existence of these two similarity solutions indicates that the frictional properties of the coupled air-sea system are easily evaluated functions of the 10-m wind speed and the peak wave period, and also leads to a simple expression for the angle of deflection of the pure drift current to the 10 m wind. The analysis provides a dynamical model for global ocean drift on monthly and annual time scales for which the steady-state neutral model is a good approximation. In particular, the theoretical results appear to be able to successfully predict the mean surface drift measured by HF Radar, which at present is the best technique for studying the near surface velocity profile.     

CYGNSS卫星风速产品的精度检验

针对 CYGNSS卫星风速产品的适用性问题,以美国国家数据浮标中心(NDBC)的实测风速与美国国家飓风中心(NHC)的最佳路径风速为参照,选取美国西南部海域为研究区,通过数据匹配和对比分析,评估了CYGNSS不同模型估算风速产品的精度。结果表明,CYGNSS FDS模型估算的中、低风速产品与NDBC浮标实测风速具有较好的一致性,CYGNSS风速与浮标风速的差异在春夏季稍高、秋冬季略低;CYGNSS YSLF模型估算的高风速产品与NHC最大风速存在较大差异,CYGNSS风速低于NHC最大风速;对于CYGNSS两种模型估算的风速产品,利用遥感观测量NBRCS反演出的风速都比LES反演出的风速具有更好的精度。总体而言,本研究验证了CYGNSS风速产品的真实有效性,对提高海洋数值预报能力具有一定的意义。     

基于观测和再分析数据的LSTM深度神经网络沿海风速预报应用研究

基于海洋气象历史观测资料和再分析数据等,利用LSTM深度神经网络方法,开展在有监督学习情况下的海面风场短时预报应用研究。以中国近海5个代表站为研究区域,通过气象台站观测数据和ERA-Interim 6 h再分析数据构建数据集。选取21个变量作为预报因子,分别构建两个LSTM深度神经网络框架(OBS_LSTM和ALL_LSTM)。经与2017年WRF模式6 h预报结果对比分析,得出如下结论:构建的两个LSTM风速预报模型可以大幅降低风速预报误差,RMSE分别降低了41.3%和38.8%,MAE平均降低了43.0%和40.0%;风速误差统计和极端大风分析发现,LSTM模型能够抓住地形、短时大风和台风等敏感信息,对于大风过程预报结果明显优于WRF模式;两种LSTM模型对比发现,ALL_LSTM模型风速预报误差最小,具有很好的稳定性和鲁棒性,OBS_LSTM模型应用范围更广泛。     

Cyclone induced storm surge and flood forecasting in the northern Bay of Bengal

A cyclone induced storm surge and flood forecasting system that has been developed for the northern Bay of Bengal is presented. The developed system includes a cyclone forecasting model that uses statistical models for forecasting of the cyclone track and maximum wind speed, and an analytical cyclone model for generation of cyclone wind and pressure fields. A data assimilation system has been developed that allows updating of the cyclone parameters based on air pressure and wind speed observations from surface meteorological stations. The forecasted air pressure and wind fields are used as input in a 2D hydrodynamic model for forecasting storm surge levels and associated flooding. An efficient uncertainty prediction procedure based on Harr's point estimation method has been implemented as part of the forecasting system for prediction of the uncertainties of the forecasted storm surge levels and inundation areas caused by the uncertainties in the cyclone track and wind speed forecasts. The developed system is applied on a severe cyclone that hit Bangladesh in April 1991. The simulated storm surge and associated flooding are highly sensitive to the cyclone data. The cyclone data assimilation system provides a more accurate cyclone track when the cyclone approaches the coastline, which results in a significant improvement of the storm surge and flood predictions. Application of the uncertainty prediction procedure shows that the large uncertainties of the cyclone track and intensity forecasts result in large uncertainties of the forecasted storm surge levels and flood extend. The forecasting system shows very good forecasting capabilities up to 24 h before the actual landfall.     

Low-frequency ambient-noise measurements in the South Fiji basin

The effect of wind speed on ambient noise has been measured in an experiment carried out in the South Fiji basin. The noise data in the band 15-250 Hz are well correlated with the variations in the local wind speed. The relationship between noise level N and wind speed ν is expressed by N=B+20n log ν. The constants B and n have been estimated by fitting the data using this model. The analysis indicates that there are two types of behavior: for ν>15 kn, a value of n=1.5 is obtained for the entire band, whereas for ν<15 kn, there is no correlation with wind speed observed in the data. The results suggest that there is a delay of 40-120 min for the effect of wind on the hydrophone noise level     

一种计算台风风场的方法

揭示Ｒａｎｋｉｎｅ涡风场模式和Ｊｅｌｅｓｎｉａｎｓｋｉ风场模式之间的联系，并设计了一种台风风场分布模式，它的风速分布曲线落在Ｊｅｌｅｓｎｉａｎｓｋｉ和Ｒａｎｋｉｎｅ涡两个风场模式的风速分布曲线之间，具有一个既优于Ｊｅｌｅｓｎｉａｎｓｋｉ又优于Ｒａｎｋｉｎｅ涡的风速衰减速率，因此它同时克服了Ｒａｎｋｉｎｅ涡模式计算风速偏小和Ｊｅｌｅｓｎｉａｎｓｋｉ模式计算风速偏大的缺点，以一种比较合理的变化趋势向远方衰减，成为一个比较切合实际的台风风场分布模式。同时，文中提出的移行台风风场计算方法对宫崎正卫、上野武夫和Ｊｅｌｅｓｎｉａｎｓｋｉ模式都有一定的改进。     

基于人工神经网络的高频雷达风速反演

风速是重要的海洋状态参数之一,对海面风速的准确提取是实现海洋环境监测和沿海工程应用的重要保证。目前,作为新兴海洋环境监测设备,高频雷达在风速提取方面仍然存在挑战。本文提出了一种基于人工神经网络的风速提取方法,利用历史浮标测量海态数据训练风速提取网络,实现风速与有效波高、波周期、风向及时间因素之间的非线性映射。测试结果表明了这一网络在时间和空间上的稳定性;进而将已训练的网络应用到便携式高频地波雷达OSMAR-S的风速反演中,得到的风速与浮标测量风速间的相关系数达到0.849,均方根误差为2.11 m/s。这一结果明显优于常规由浪高反演风速的SMB方法,验证了该方法在高频雷达风速反演中的可行性。     

基于同步风场的机载SAR海浪参数反演方法

针对机载合成孔径雷达(SAR)对海探测特点,采用多入射角法从SAR数据本身得到与海浪参数反演区域时空匹配的同步海面风速和风向,并结合线性变换关系,计算得到海浪初猜谱对应的仿真SAR图像谱,将仿真SAR图像谱和观测SAR图像谱输入代价函数中进行迭代运算,通过非线性方程的解算得到最适海浪谱;采用交叉谱法去除海浪传播180°方向模糊,最终得到海浪参数。论文提出的基于同步风场的机载SAR海浪参数反演方法,充分利用了机载SAR海洋环境探测的优势,解决了传统SAR海浪参数反演中初猜谱构造依赖外部风场的问题,机载同步飞行试验的海浪参数反演结果与浮标观测值的有效波高、波向的均方根误差分别为0.23 m和13.23°,验证了该方法的有效性,可为机载SAR海浪参数反演业务化提供支持。     

利用西北印度洋船测数据评估基于卫星的海表面温度

本文描述了一次夏季在西北印度洋进行的调查船水文测量,用船测数据评估卫星海面表温度,并寻找影响海表面温度误差的主要因素。我们考虑了两种卫星数据,第一种是微波遥感产品——热带降雨测量任务微波成像仪TMI数据,另外一种是融合了微波,红外线,以及少部分观测数据的融合数据产品——可处理海表温度和海冰分析OSTIA数据。结果表明融合数据的日平均海表面温度的平均误差和均方根误差都比微波遥感小。这一结果证明了融合红外线遥感,微波遥感以及观测数据来提高海表面温度数据质量的必要性。此外,我们分析了海表面温度误差与各项水文参数之间的相关关系,包括风速,大气温度,想对湿度,大气压力,能见度。结果表明风速与TMI海表面温度误差的相关系数最大。而大气温度是影响OSTIA海表面温度误差最重要的因素;与此同时,想对湿度与海表面温度误差的相关系数也很高。     

天津沿海风暴潮实时监测预报系统

本文简要介绍了天津沿海风暴潮实时监测预报系统建设的必要性和系统的结构、功能、组成等方面的概况。该系统将实时监测技术、计算机网络技术与信息处理技术有效地结合起来，实现了潮汐、风速、风向等监测参数在无人值守情况下的连续、自动观测和计算机网络系统的实时接入与发布。它的建立一方面可为管理部门、预报部门和生产部门提供实时的监测信息，另一方面可在风暴潮来临时为领导决策提供历史资料和预报信息。它的应用对减少海洋灾害的损失，促进风暴潮减灾防灾体系的建设起到了积极的作用。     

Temporal significant wave height estimation from wind speed by perceptron Kalman filtering

Significant wave height estimates are necessary for many applications in coastal and offshore engineering and therefore various estimation models are proposed in the literature for this purpose. Unfortunately, most of these models provide simultaneous wave height estimations from wind speed measurements. However, in practical studies, the prediction of significant wave height is necessary from previous time interval measurements. This paper presents a dynamic significant wave height prediction procedure based on the perceptron Kalman filtering concepts. Past measurements of significant wave height and wind speed variables are used for training the adaptive model and it is then employed to predict the significant wave height amounts for future time intervals from the wind speed measurements only. The verification of the proposed model is achieved through the dynamic significant wave height and wind speed time series plots, observed versus predicted values scatter diagram and the classical linear significant wave height models. The application of the proposed model is presented for a station in USA.     

全极化合成孔径雷达近岸风场反演研究

Coastal winds are strongly influenced by topology and discontinuity between land and sea surfaces. Wind assessment from remote sensing in such a complex area remains a challenge. Space-borne scatterometer does not provide any information about the coastal wind field, as the coarse spatial resolution hampers the radar backscattering. Synthetic aperture radar （SAR） with a high spatial resolution and all-weather observation abilities has become one of the most important tools for ocean wind retrieval, especially in the coastal area. Conventional methods of wind field retrieval from SAR, however, require wind direction as initial information, such as the wind direction from numerical weather prediction models （NWP）, which may not match the time of SAR image acquiring. Fortunately, the polarimetric observations of SAR enable independent wind retrieval from SAR images alone. In order to accurately measure coastal wind fields, this paper proposes a new method of using co-polarization backscattering coefficients from polarimetric SAR observations up to polarimetric correlation backscattering coefficients, which are acquired from the conjugate product of co-polarization backscatter and cross-polarization backscatter. Co-polarization backscattering coefficients and polarimetric correlation backscattering coefficients are obtained form Radarsat-2 single-look complex （SLC） data.The maximum likelihood estimation is used to gain the initial results followed by the coarse spatial filtering and fine spatial filtering. Wind direction accuracy of the final inversion results is 10.67 with a wind speed accuracy of 0.32 m/s. Unlike previous methods, the methods described in this article utilize the SAR data itself to obtain the wind vectors and do not need external wind directional information. High spatial resolution and high accuracy are the most important features of the method described herein since the use of full polarimetric observations contains more information about the space measured.This article is a useful addition to the work of independent SAR wind retrieval. The experimental results herein show that it is feasible to employ the co-polarimetric backscattering coefficients and the polarimetric correlation backscattering coefficients for coastal wind field retrieval.     

Mapping offshore wind resources: synergetic potential of SAR and scatterometer data

An accurate evaluation of the wind potential is needed to assess the economic reliability of an offshore wind project and to site wind farms, which need high-spatial-resolution wind fields with high repetitiveness. A measurement campaign is highly expensive, and the in situ data obtained are insufficient: the data time series are too short, and only defined at a single location. Moreover, the wind-flow models usually used, are unable to accurately extrapolate the data from the mast over the entire area of interest. Fortunately, wind fields at sea can be measured by active spaceborne sensors, such as synthetic aperture radar (SAR) and scatterometer sensors. This paper aims at evaluating the adequacy of wind data from space to the data requirements of the offshore wind industry. The different types of data available from space are described. The temporal sampling of the satellite data is evaluated compared to user needs. The adequacy of this set of information for Weibull-distribution estimation is demonstrated. A study area has been selected to illustrate the approach, namely the French Gulf of Lion area.     

Effects of variable winds on biological productivity on continental shelves in coastal upwelling systems

The production and distribution of biological material in wind-driven coastal upwelling systems are of global importance, yet they remain poorly understood. Production is frequently presumed to be proportional to upwelling rate, yet high winds can lead to advective losses from continental shelves, where many species at higher trophic levels reside. An idealized mixed-layer conveyor (MLC) model of biological production from constant upwelling winds demonstrated previously that the amount of new production available to shelf species increased with upwelling at low winds, but declined at high winds [Botsford, L.W., Lawrence, C.A., Dever, E.P., Hastings, A., Largier, J., 2003. Wind strength and biological productivity in upwelling systems: an idealized study. Fisheries Oceanography 12, 245–259]. Here we analyze the response of this model to time-varying winds for parameter values and observed winds from the Wind Events and Shelf Transport (WEST) study region. We compare this response to the conventional view that the results of upwelling are proportional to upwelled volume. Most new production per volume upwelled available to shelf species occurs following rapid increases in shelf transit time due to decreases in wind (i.e. relaxations). However, on synoptic, event time-scales shelf production is positively correlated with upwelling rate. This is primarily due to the effect of synchronous periods of low values in these time series, paradoxically due to wind relaxations. On inter-annual time-scales, computing model production from wind forcing from 20 previous years shows that these synchronous periods of low values have little effect on correlations between upwelling and production. Comparison of model production from 20 years of wind data over a range of shelf widths shows that upwelling rate will predict biological production well only in locations where cross-shelf transit times are greater than the time required for phytoplankton or zooplankton production. For stronger mean winds (narrower shelves), annual production falls below the peak of constant wind prediction [Botsford et al., 2003. Wind strength and biological productivity in upwelling systems: an idealized study. Fisheries Oceanography 12, 245–259], then as winds increase further (shelves become narrower) production does not decline as steeply as the constant wind prediction.     

黄河三角洲海上风场再分析资料对比

再分析风场资料在海洋气象的研究中得到广泛应用.本文基于黄河口区域孤东59井验潮站和桩西106验潮站现场观测资料,对CCMP(cross-calibrated multi-platform)、CFSR(climate forecast system re-analysis)、ERA-interim、JRA-554种再分析...     

Measurement of the sea surface emissivity

The sea surface emissivity in the infrared region is determined on the basis of data analyses. Net radiation, surface irradiance and other oceanographical and meteorological variables are measured throughout most of the year at the oceanographical observatory tower in Tanabe Bay, Japan. We have found that 0.984±0.004 is a reliable emissivity value from the night time data. Surface emission radiates not from the subsurface water but from the sea surface. The thermal skin layer on the sea surface, however, is disturbed and disappears under high wind speed over 5 m/s through the analyses of the radiation observation using the emissivity value of 0.984. Under low wind speed, the sea surface can be cooler or warmer than the subsurface due to overlying thermal conditions and the skin layer can be neutral as the transient process between them. By using an emissivity value of 0.984, the temperature difference between the sea surface temperature and the temperature determined from surface irradiance that has been reported in the satellite data analyses is found to be reduced by half.