Evaluation of ENVISAT ASAR data for sea surface wind retrieval in Hong Kong coastal waters of China

The C-band wind speed retrieval models, CMOD4, CMOD - IFR2, and CMOD5 were applied to retrieval of sea surface wind speeds from ENVISAT （European environmental satellite） ASAR （advanced synthetic aperture radar） data in the coastal waters near Hong Kong during a period from October 2005 to July 2007. The retrieved wind speeds are evaluated by comparing with buoy measurements and the QuikSCAT （quick scatterometer） wind products. The results show that the CMOD4 model gives the best performance at wind speeds lower than 15 m/s. The correlation coefficients with buoy and QuikSCAT winds are 0.781 and 0.896, respectively. The root mean square errors are the same 1.74 m/s. Namely, the CMOD4 model is the best one for sea surface wind speed retrieval from ASAR data in the coastal waters near Hong Kong.     

A Spectral Approach for Determining Altimeter Wind Speed Model Functions

We propose a new analytical algorithm for the estimation of wind speeds from altimeter data using the mean square slope of the ocean surface, which is obtained by integration of a widely accepted wind-wave spectrum including the gravity-capillary wave range. It indicates that the normalized radar cross section depends not only on the wind speed but also on the wave age. The wave state effect on the altimeter radar return becomes remarkable with increasing wind speed and cannot be neglected at high wind speeds. A relationship between wave age and nondimensional wave height based on buoy observational data is applied to compute the wave age using the significant wave height of ocean waves, which could be simultaneously obtained from altimeter data. Comparison with actual data shows that this new algorithm produces more reliable wind speeds than do empirical algorithms. This revised version was published online in July 2006 with corrections to the Cover Date.     

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。     

近海海上风电场水下噪声传播模型适用性研究

通过现场采集近海海上风电场工程区运营期风机水下噪声和背景噪声数据,计算了噪声信号的倍频带声压级,功率谱级和峰值声压级,确定了海上风电场水下噪声总声源级为148.3 d B,以此开展近海海上风电工程风机水下噪声频域特性、功率密度谱特性等研究。在此基础上使用Kraken简正波模型和Bellhop射线模型对风电场运营期风机水下噪声在水平与垂直方向上的传播进行模拟,模拟了噪声在不同频带内的衰减程度,结果显示模型模拟结果在不同频率下的衰减趋势有着很大差异,产生了明显的多途干涉现象,通过实测数据对建立的噪声传播模型进行验证,发现Kraken简正波模型在500 Hz以下,Bellhop射线模型在500 Hz以上适合模拟实际水下噪声传播情形,同时海区本身背景噪声的存在会对预测的准确性产生影响。这些结论可用于进一步对近海海上风电场水下噪声传播的研究。     

Wind speed inversion and in-orbit assessment of the imaging altimeter on Tiangong-2 space station

Imaging altimeter (IALT) is a new type of radar altimeter system. In contrast to the conventional nadir-looking altimeters, such as HY-2A altimeter, Jason-1/2, and TOPEX/Poseidon, IALT observes the earth surface at low incident angles (2.5°–8°), so its swath is much wider and its spatial resolution is much higher than the previous altimeters. This paper presents a wind speed inversion method for the recently launched IALT onboard Tiangong-2 space station. Since the current calibration results of IALT do not agree well with the well-known wind geophysical model function at low incidence angles, a neural network is used to retrieve the ocean surface wind speed in this study. The wind speed inversion accuracy is evaluated by comparing with the ECMWF reanalysis wind speed, buoy wind speed, and in-situ ship measurements. The results show that the retrieved wind speed bias is about –0.21 m/s, and the root-mean-square (RMS) error is about 1.85 m/s. The wind speed accuracy of IALT meets the performance requirement.     

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C-2PO模型和CMOD5.N地球物理模式函数的SAR风速反演性能评估

本文选取142幅RADARSAT-2全极化合成孔径雷达（SAR）影像,在没有入射角输入的情况下,首先利用C-2PO模型进行海面风速反演。随后,将同一时空下的ASCAT散射计风向作为初始风向,提取相应雷达入射角,利用地球物理模式函数（GMF） CMOD5.N对142幅SAR影像进行风速计算。反演结果与美国国家资料浮标中心海洋浮标风速数据对比,结果显示:CMOD5.N GMF和C-2PO模型均可反演出较高精确度的海面风速,其均方根误差分别为1.68 m/s和1.74 m/s。此外,研究发现,在低风速段,CMOD5.N GMF的风速反演精度要明显优于C-2PO模型。针对这一现象,本文以SAR系统成像机理为基础,以低风速SAR图像为具体案例,给出了3种造成这一现象的原因。     

GPS浮标数据反演海浪谱的理论仿真与试验验证

GPS浮标作为一种新型的海洋测量设备,近年来在海面高度现场测量和星载高度计定标方面取得了重要应用。通过仿真试验对反演海浪谱的方法和流程进行研究,旨在探索从GPS浮标测量的海面高度序列中提取海浪谱的方法。首先,使用Longuest模型生成了海浪波面位移时间序列,并通过Pierson-Moscowitz风浪谱对波面位移的统计特性进行约束,其随机性由相位引入。结合典型潮汐和GPS浮标仪器噪声的仿真时间序列,合成了仿真时间长度1h的1Hz(每秒1次)随机海面高度序列。然后,利用自相关函数法,进行高通滤波和数据压缩,得到了仿真的海浪谱。该仿真结果和理论海浪谱非常接近,可满足海浪谱反演的需求。最后,通过山东石岛外海的GPS浮标现场试验,验证了本文提出的反演方法的适用性。本文的研究解决了GPS浮标反演海浪谱的关键问题,丰富了海浪谱反演的手段,拓展了GPS浮标的应用领域,有望为未来我国的星载波谱仪定标服务。     

Sea surface wind speed retrieval from Sentinel-1 HH polarization data using conventional and neural network methods

Conventional retrieval and neural network methods are used simultaneously to retrieve sea surface wind speed(SSWS) from HH-polarized Sentinel-1(S1) SAR images. The Polarization Ratio(PR) models combined with the CMOD5.N Geophysical Model Function(GMF) is used for SSWS retrieval from the HH-polarized SAR data. We compared different PR models developed based on previous C-band SAR data in HH-polarization for their applications to the S1 SAR data. The recently proposed CMODH, i.e., retrieving SSWS directly from the HHpolarized S1 data is also validated. The results indicate that the CMODH model performs better than results achieved using the PR models. We proposed a neural network method based on the backward propagation(BP)neural network to retrieve SSWS from the S1 HH-polarized data. The SSWS retrieved using the BP neural network model agrees better with the buoy measurements and ASCAT dataset than the results achieved using the conventional methods. Compared to the buoy measurements, the bias, root mean square error(RMSE) and scatter index(SI) of wind speed retrieved by the BP neural network model are 0.10 m/s, 1.38 m/s and 19.85%,respectively, while compared to the ASCAT dataset the three parameters of training set are –0.01 m/s, 1.33 m/s and 15.10%, respectively. It is suggested that the BP neural network model has a potential application in retrieving SSWS from Sentinel-1 images acquired at HH-polarization.     

OSMAR-S100便携式高频地波雷达海浪和海面风探测性能分析

OSMAR-S系列便携式高频地波雷达系统采用单极子/交叉环紧凑型天线阵,通过单站雷达即可实现有效探测距离约10km内海浪和海面风的单点观测。为了更好地了解OSMAR-S100雷达系统海浪和海面风的综合探测性能,于2013年1月29日至3月7日在台湾海峡西南部海域进行了雷达与浮标观测的对比试验,得到了有效波高、有效波周期、平均风速和平均风向数据。对比结果表明,OSMAR-S100便携式高频地波雷达可有效观测距雷达10km以内有效波高0.5m以上的海浪平均状况和平均风速5m/s以上的海面风,雷达反演有效波高和有效波周期的均方根误差分别为0.60m和1.60s,反演平均风速和平均风向的均方根误差为1.83m/s和16.7°。在未经区域化标定的情况下,此结果说明了该型雷达产品已初步具备了海浪和海面风的业务化观测水平。     

Directional spread parameter at intermediate water depth

The characteristics of directional spread parameters at intermediate water depth are investigated based on a cosine power ‘2s' directional spreading model. This is based on wave measurements carried out using a Datawell directional waverider buoy in 23 m water depth. An empirical equation for the frequency dependent directional spreading parameter is presented. Directional spreading function estimated based on the Maximum Entropy Method is compared with those obtained using a cosine power ‘2s' parameter model. A set of empirical equations relating the directional spreading parameter corresponding to the peak of wave spectrum to other wave parameters like significant wave height and period are obtained. It shows that the wave directional spreading at peak wave frequency can be related to the non-linearity parameter, which allows estimation of directional spreading without reference to wind information.     

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。     

Measurement of oceanic wind speed from HF sea scatter by skywave radar

Remote measurements of the spatial mean ocean wind speeds were obtained using Doppler spectra resolved to 0.08 Hz from high-resolution HF skywave-radar backscatter measurements of the ocean surface. A standard deviation of 2.4 m/s resulted from the correlation of observed winds over the ocean and the broadening of the Doppler spectra in the vicinity of the higher first-order Bragg line. This broadening, for Doppler spectra unperturbed by the ionospheric propagation, is proportional to the increase in power caused by higher order hydrodynamic and electromagnetic effects in the vicinity of the Bragg line and inversely proportional to the square root of the radio frequency. A lower bound on the measure of wind speed was established at 5 m/s by the low resolution spectral processing and low second-order power. An upper limit is suggested by the steep slope in the region of the sea backscatter spectrum outside the square root of two times the first-order Bragg line Doppler.     

GNSS-R观测下的海面飓风风速反演

利用全球导航卫星系统在地球表面的反射信号（GNSS-R）进行海面风速反演已经被广泛研究并作为一种重要的遥感手段。目前,该L波段微波信号的相关功率已可以在多普勒频率和时延码片空间进行多普勒时延图像的成像。由于该图像的图像特征与海面粗糙度有较高的相关性,因此能够用来进行海面风场反演。然而,对于该遥感手段而言,其双基雷达前向散射截面（BRCS）理论上与海面粗糙度有更高的相关性,如同目前合成孔径雷达使用后向雷达散射截面而非相关功率。所以,本文通过改进已有的GNSS-R的双基雷达散射截面方程,代替相关功率在多普勒时延空间进行成像,得出了与海面粗糙度相关的双基雷达散射截面图像（BRCS map）。基于该图像,本文提出了三种与其形状特征相关的观测量,通过2005年Dennis飓风GNSS-R机载数据生成的16000多幅图像进行地球物理模式函数建模并与经典的一维时延波形匹配方法得出结果进行对比分析,得出更为精确的风速反演结果。     

Extraction of Ocean Wave Spectra From Simulated Noisy Bistatic High-Frequency Radar Data

An algorithm is developed for the inversion of bistatic high-frequency (HF) radar sea echo to give the nondirectional wave spectrum. The bistatic HF radar second-order cross section of patch scattering, consisting of a combination of four Fredholm-type integral equations, contains a nonlinear product of ocean wave directional spectrum factors. The energy inside the first-order cross section is used to normalize this integrand. The unknown ocean wave spectrum is represented by a truncated Fourier series. The integral equation is then converted to a matrix equation and a singular value decomposition (SVD) method is invoked to pseudoinvert the kernel matrix. The new algorithm is verified with simulated radar Doppler spectrum for varying water depths, wind velocities, and radar operating frequencies. To make the simulation more realistic, zero-mean Gaussian noise from external sources is also taken into account     

Directional sea spectrum determination using HF Doppler radar techniques

Second-order features in HF radar Doppler spectral data are compared with a theoretical model of the radar spectrum. The model is the corner reflector double-scatter model which employs a more realistic directional sea spectrum model than those used in earlier works. It includes a frequency-dependent angular spreading function and assumes the existence of spectral energy over a full360degarising from an apparent second-order wave-wave interaction. Comparison is made with ground wave data collected at the NRL/NOAA/ITS San Clemente Island HF radar.     

一种新型激光雷达测风浮标设计及海试应用

精确获取剖面风数据对海上风电场规划选址、风能资源评估等至关重要。针对测风塔建设成本高、拆卸移动困难,而传统浮标电源供给较小、摇晃角度大等缺点,设计一种新型激光雷达浮标,实现了对目标海域的风资源剖面数据的观测、解算与自存储。在南海海域进行海试应用,并对观测的剖面风数据和传统大型海洋资料浮标风数据进行同步比测分析。结果表明,激光雷达测风浮标的测量结果与大型浮标测风数据吻合较好,风速R~2相关系数达到0.975,风向R~2相关系数达到0.932。该研究成果可为激光雷达测风浮标在海上风电开发和开展风场变化的科学研究等提供参考。     

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。     

强台风“卡努”过境期间的风和海浪特征分析

南海北部海域夏季台风活动频繁,对海上生产活动和人民生命财产安全造成极大威胁,由于台风路径的不确定性,其中心附近区域的风浪观测资料十分稀少。中国气象局(China Meteorological Administration, CMA)热带气旋最佳路径数据显示2017年10月强台风“卡努”中心经过南海北部陆坡的SF301浮标,该浮标完整记录了台风过境的风浪数据。利用浮标观测资料,分析了强台风“卡努”过境期间的风和海浪特征。观测结果表明,“卡努”经过浮标时,中心气压为959.9 hPa,风速随时间呈双峰分布,前、后眼壁区的10 min平均风速分别为30.2 m/s和24.9 m/s, 1 s极大风速分别为44.2和38.6 m/s。海浪以风浪为主,观测有效波高和最大波高最大值分别为10.8和14.3 m,滞后最大风速30 min,波向和风向变化趋势一致。台风过境期间,有效波高与海面10 m风速接近线性关系,非台风期间二者呈二次多项式关系。海浪无因次波高和周期呈幂指数关系,无论是台风期间还是非台风期间二者关系十分接近Toba提出的3/2指数律。     

Evaluation of marine surface winds observed by SeaWinds and AMSR on ADEOS-II

Marine surface winds observed by two microwave sensors, SeaWinds and Advanced Microwave Scanning Radiometer (AMSR), on the Advanced Earth Observing Satellite-II (ADEOS-II) are evaluated by comparison with off-shore moored buoy observations. The wind speed and direction observed by SeaWinds are in good agreement with buoy data with root-mean-squared (rms) differences of approximately 1 m s⁻¹ and 20°, respectively. No systematic biases depending on wind speed or cross-track wind vector cell location are discernible. The effects of oceanographic and atmospheric environments on the scatterometry are negligible. Though the wind speed observed by AMSR also showed agreement with buoy observations with rms difference of 1.27 m s⁻¹, the AMSR wind speed is systematically lower than the buoy data for wind speeds lower than 5 m s⁻¹. The AMSR wind seems to have a discontinuous trend relative to the buoy data at wind speeds of 5–6 m s⁻¹. Similar results have been obtained in an intercomparison of wind speeds globally observed by SeaWinds and AMSR on the same orbits. A global wind speed histogram of the AMSR wind shows skewed features in comparison with those of SeaWinds and European Centre for Medium-range Weather Forecasts (ECMWF) analyses.