神经网络反演散射计风场算法的研究

建立了一个神经网络反演卫星散射计海面风场的B-P算法,给出了一个神经网络反演风场的模型,并利用该反演算法和模型对实际卫星散射计数据进行了海面风场反演试验,对风向的多解性利用圆中数滤波方法进行排除.对神经网络训练和检验数据集分别采用ERS-1/2散射计数据和欧洲中期天气预报(ECMWF)提供的风场作为配准点数据.把反演的风速和风向与CMCD4和ECMWF的风场作了比较,它们吻合得比较好;研究表明神经网络反演海面风场是可行和高效的.     

基于混合密度网络的NSCAT散射计海面风场反演

以NSCAT散射计数据为例,介绍了一种神经网络反演海面风场的方法.风速的反演是基于多层感知器网络;多解风向的反演是基于多层感知器网络和混合密度模型组合而成的混合密度网络,其中的核函数采用高斯函数的形式.通过与欧洲中期天气预报模式风场和现场浮标数据对比,证明了该神经网络反演海面风场的有效性.     

一种改进的扇形波束旋转扫描散射计海面风矢量反演算法

扇形波束旋转扫描散射计（RFSCAT）是约十年前才被提出来的一种新型星载微波散射计。与其它旋转扫描散射计类似,其星下点附近区域和刈幅边缘区域的风场反演误差相对较大。在本文设定的参数条件下,RFSCAT散射计刈幅边缘区域的风向反演精度相对于轨道中间区域降低了约9°。针对这一问题,本文为RFSCAT散射计提出了一种改进的风矢量反演算法。新算法的主要特征是,根据风向反演偏差直方图,在整个刈幅区域内,对模糊解风向取值区间进行自适应扩展,以获取并保留更多可能风向解。利用模拟的100条轨道的L2A数据,对新算法进行反演验证。实验结果证明,新算法能够有效改善RFSCAT散射计星下点附近区域和轨道刈幅边缘区域的风向反演精度。星下点和刈幅边缘上的风矢量单元的风向反演精度相对于标准的MLE算法分别提高了1.6°和9°。     

HY-2B卫星散射计海面风场产品质量分析 *

星载微波散射计是获取全球海面风场信息的主要手段, HY-2B卫星散射计的成功发射为全球海面风场数据获取的持续性提供了重要保障。本文利用欧洲中期天气预报中心(European Center for Medium-Range Weather Forecasts, ECMWF)再分析风场数据、热带大气海洋观测计划(Tropical Atmosphere Ocean Array, TAO)和美国国家数据浮标中心(National Data Buoy Center, NDBC)浮标获取的海面风矢量实测数据, 对HY-2B散射计海面风场数据产品的质量进行统计分析。分析表明, HY-2B风场与ECMWF再分析风场对比, 在4~24m·s^-1风速区间内, 风速和风向均方根误差(root mean square error, RMSE)分别为1.58m·s^-1和15.34°; 与位于开阔海域的TAO浮标数据对比, 风速、风向RMSE分别为1.03m·s^-1和14.98°, 可见HY-2B风场能较好地满足业务化应用的精度要求(风速优于2m·s^-1, 风向优于20°)。与主要位于近海海域的NDBC浮标对比, HY-2B风场的风速、风向RMSE分别为1.60m·s^-1和19.14°, 说明HY-2B散射计同时具备了对近海海域风场的良好观测能力。本文还发现HY-2B风场质量会随风速、地面交轨位置等变化, 为用户更好地使用HY-2B风场产品提供参考。     

星载散射计资料反演带有气旋及锋面的复杂风场

气象预报在确定热带气旋中的大风（风速17．2～20．7m／s）半径时，由于热带海洋地区在恶劣气候下缺乏现场数据，往往过高地估计了大风半径。星载散射计在探测海面上的风矢量的同时，也探测到许多热带气旋。本文利用Seasat－A卫星上的散射计（SASS）资料反演计算海面风暴的风矢量，并利用风速等值线进行风向多解排除，计算结果表明：星载散射计能探测到大风的阈值（17～18m／s），其准确度为2m／s或±10％（取大者）。     

机载多普勒散射计海风海流联合反演研究

多普勒散射计能够获取海面后向散射系数和多普勒频移,从而实现海面风场和海表流场的同步观测。本文基于机载多普勒散射计的观测数据,对多普勒散射计海面风场和海表流场联合反演模型进行研究,并与风场流场独立反演结果进行对比。结果表明,联合反演的流场精度显著优于独立反演结果;然而以欧洲中期天气预报中心的海面风场为参考时,联合反演风场的精度略低于独立反演结果。这说明多普勒频移信息对海面风场反演的贡献不太显著,但雷达后向散射系数信息（即风场）对流场反演有积极的作用,通过联合反演算法能够更有效地消除海面风场对流场反演的影响。研究结果有助于进一步理解海面风场和海表流场反演时的相互影响,并为星载多普勒散射计的数据处理提供了参考。     

A study of a new retrieval algorithm for measurement of oceanic windvectors field using satellite microwave scatterometer

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Construction of surface wind stress fields with high temporal resolution using ERS-1 scatterometer data

Wind stress fields with high temporal resolution over the North Pacific have been constructured by using ERS-1 scatterometer data. A simple objective analysis, a successive correction method, was used to construct the fields. Several necessary parameters used in the method are examined by a simulation based on the climatological data. The meridional decorrelation scale of the wind stress depends strongly on the season, while the zonal decorrelation scale is highly stable. We determined the decorrelation scale depending on the location and the time and applied to the successive correction method. The monthly mean field constructed by averaging the daily mean data is free from an aliasing error, which is a serious problem if a simple monthly averaging is applied. The daily wind stress data obtained in the present study represent small time- and spatial-scale variation and large amplitudes compared with data interpolated from simple monthly mean data. The satellite-derived data are also compared with in situ data obtained by meteorological buoys. The satellite wind speeds are lower than in situ wind speeds for every buoy. This underestimation is not due to the present objective analysis, but due to the original data, the ERS-1 Scatterometer Value-Added Product.     

中法海洋卫星微波散射计风场反演残差特性研究

2018年10月发射的中法海洋卫星散射计(CSCAT)是国际上首个扇形波束旋转扫描微波散射计。本文以最大似然估计风场反演算法为基线,详细分析了中法海洋卫星微波散射计海面风场反演代价函数的残差特性,重点研究了新的观测几何对风场反演残差以及风场质量的影响,并建立了风场模糊解的似然概率模型函数。结果表明,CSCAT风场反演的残差特性随风矢量单元在刈幅交轨方向位置的变化而变化,模糊解似然概率模型函数的指数分布在−0.4～−1.8之间。分析结果为CSCAT风场质量控制和二维变分分析去模糊算法的精细化调整提供了重要的参考。     

基于微波散射计观测的气候态海面风场和风应力场

收集了星载微波散射计NSCAT,QuikSCAT和SeaWinds on ADOES-II的全球海面风速和方向L2B数据,数据涉及的时间序列长度为11.5 a。通过对所收集数据的质量控制、Loess低通空间滤波和统计处理,构建了气候态的逐月全球海面风场、风应力场和风应力旋度场(简称为SCAT),其空间网格间距为0.25°×0.25°。SCAT资料与其他有关资料相比,包含了更丰富的海面风场中小尺度空间变化的信息,可广泛应用于海洋、气候、海气相互作用等方面的研究,特别适合应用于海洋中小尺度过程的研究。作为我国"海洋二号"("HY-2")卫星预研项目的成果之一,SCAT资料将由国家海洋局国家卫星海洋应用中心提供给有关用户。     

基于HY-2A微波散射计海面风场资料的台风风剖面信息提取研究

台风风剖面信息是直观反映与台风中心不同距离的各点与平均风速关系的曲线,它是确定各级台风风圈范围的重要基础。本文利用HY-2A微波散射计海面风场资料,结合Holland风场模型提出了一种新的台风风剖面信息提取方法,并选取2012–2017年期间16期典型台风进行应用。结果表明:34 kt与50 kt风圈半径的平均均方根误差为37.6 km与18.3 km,该方法具有较好的适用性和精度。本研究对于描述台风结构特征及潜在的破坏力和台风可能的影响范围具有一定的现实意义。     

Validation and accuracy analysis of wind products from scatterometer onboard the HY-2B satellite

The Chinese marine dynamic environment satellite HY-2B was launched in October 2018 and carries a Ku-band scatterometer. This paper focuses on the accuracies of HY-2B scatterometer wind data during the period from November 2018 to May 2021. The HY-2B wind data are validated against global moored buoys operated by the U.S. National Data Buoy Center and Tropical Atmosphere Ocean, numerical model data by the National Centers for Environmental Prediction, and the Advanced Scatterometer data issued b...     

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.     

星载全极化微波散射计仿真与海面风场反演研究

通过建立全极化微波散射计系统仿真模型,探索全极化微波散射计的风场反演方法;通过对比不同仪器测量精度下全极化和同极化微波散射计风场反演结果,分析评价全极化微波散射计系统反演海面风场的性能.全极化微波散射计通过增加测量信息来减少模糊解出现的概率,进一步提升风场反演精度.结果表明全极化微波散射计相比同极化微波散射计具有更好的风场反演性能,对于风向结果的改善较为明显:在仿真实验中,全极化仿真反演的风速误差结果优于同极化10°以上,证明了全极化微波散射计能够提升风场反演性能.该仿真结果对我国后续海洋卫星的研发具有一定的借鉴作用.     

基于HY-2A散射计数据的极区海冰范围反演方法及其评估

A sea ice extent retrieval algorithm over the polar area based on scatterometer data of HY-2A satellite has been established.Four parameters are used for distinguishing between sea ice and ocean with Fisher's linear discriminant analysis method.The method is used to generate polar sea ice extent maps of the Arctic and Antarctic regions of the full 2013–2014 from the scatterometer aboard HY-2A(HY-2A-SCAT) backscatter data.The time series of the ice mapped imagery shows ice edge evolution and indicates a similar seasonal change trend with total ice area from DMSP-F17 Special Sensor Microwave Imager/Sounder(SSMIS) sea ice concentration data.For both hemispheres,the HY-2A-SCAT extent correlates very well with SSMIS 15% extent for the whole year period.Compared with Synthetic Aperture Radar(SAR) imagery,the HY-2A-SCAT ice extent shows good correlation with the Sentinel-1 SAR ice edge.Over some ice edge area,the difference is very evident because sea ice edges can be very dynamic and move several kilometers in a single day.     

海浪对ASCAT散射计反演风场的影响研究

To improve retrieval accuracy, this paper studies wave effects on retrieved wind field from a scatterometer. First, the advanced scatterometer(ASCAT) data and buoy data of the National Data Buoy Center(NDBC) are collocated. Buoy wind speed is converted into neutral wind at 10 m height. Then, ASCAT data are compared with the buoy data for the wind speed and direction. Subsequently, the errors between the ASCAT and the buoy wind as a function of each wave parameter are used to analyze the wave effects. Wave parameters include dominant wave period(dpd), significant wave height(swh), average wave period(apd) and the angle between the dominant wave direction(dwd) and the wind direction. Collocated data are divided into sub-datasets according to the different intervals of each wave parameter. A root mean square error(RMSE) for the wind speed and a mean absolute error(MAE) for the wind direction are calculated from the sub-datasets, which are considered as the function of wave parameters. Finally, optimal wave conditions on wind retrieved from the ASCAT are determined based on the error analyses. The results show the ocean wave parameters have correlative relationships with the RMSE of the retrieved wind speed and the MAE of the retrieved wind direction. The optimal wave conditions are presented in terms of dpd, swh, apd and angle.     

一种温度相关的HY-2A散射计地球物理模型函数

利用散射计测量海面后向散射系数, 并通过地球物理模型函数(geophysical model function, GMF)反演得到海面风场。目前散射计风场反演所采用的GMF一般只考虑雷达极化方式、雷达入射角、风速和相对风向对海面后向散射系数的影响, 而相关研究表明海表温度(sea surface temperature, SST)对Ku波段散射计风场反演具有不可忽略的影响。文章利用海洋二号A卫星散射计(Haiyang-2A Scatterometer, HY2A-SCAT)后向散射系数观测值、欧洲中期天气预报中心(European Center for Medium-Range Weather Forecasts, ECMWF )再分析风矢量和SST数据, 采用人工神经网络方法, 建立起一种SST相关的GMF (TNGMF)。对TNGMF进行分析后发现, 海面后向散射系数随着SST的增加而增加, 并且其增加幅度与雷达极化方式、风速有关。为了对比, 文章使用相同数据集和相同方法建立了不包含SST的GMF (NGMF), 将美国国家航天航空局散射计-2 (National Aeronautics and Space Administration Scatterometer-2, NSCAT2) GMF、TNGMF和NGMF分别用于HY2A-SCAT风场反演实验。试验结果表明, 采用NSCAT2 GMF、NGMF反演得到的风速在低温时系统性偏小, 在高温时系统性偏大; 而TNGMF可较好地纠正SST对风速偏差均值的影响, 从而提高反演风场质量。     

基于HY-2B散射计的热带气旋定位定强研究

热带气旋灾害是最严重的自然灾害之一,其影响程度主要取决于气旋的中心位置和强度。提高热带气旋中心位置及强度监测水平对于改进热带气旋分析预报精度、减少热带气旋的灾害影响具有重要意义。本文以HY-2B散射计为例,分析了散射计风场散度和旋度的分布特征,发现气旋中心附近风场的散度或旋度具有显著的分布规律,由此提出了一种新的气旋中心定位方法,并与传统的直接定位法进行比较研究。在此基础上,进一步提出了热带气旋风圈大小估计的方法,用于评估气旋的强度。最后,利用台风“范斯高”和“博罗依”的遥感数据对文中的方法进行验证,结果表明,使用新方法定位的气旋中心位置与最佳路径之间的差异小于20 km,散射计17 m/s风圈大小的变化一定程度上反映了气旋的发展规律。     

Comparison of sea surface wind field measured by HY-2A scatterometer and WindSat in global oceans

In this study, we present a comprehensive comparison of the sea surface wind ?eld measured by scatterometer(Ku-band scatterometer) aboard the Chinese HY-2 A satellite and the full-polarimetric radiometer WindSat aboard the Coriolis satellite. The two datasets cover a four-year period from October2011 to September 2015 in the global oceans. For the sea surface wind speed, the statistical comparison indicates good agreement between the HY-2 A scatterometer and WindSat with a bias of nearly 0 m/s and a root mean square error(RMSE) of 1.13 m/s. For the sea surface wind direction, a bias of 1.41° and an RMSE of 20.39° were achieved after excluding the data collocated with opposing directions. Furthermore,discrepancies in sea surface wind speed measured by the two sensors in the global oceans were investigated.It is found that the larger dif ferences mainly appear in the westerlies in the both hemispheres. Both the bias and RMSE show latitude dependence, i.e., they have signi?cant latitudinal ?uctuations.     

星载激光雷达卫星海面风场反演算法研究

The principal purpose of this paper is to extract entire sea surface wind＇s information from spaceborne lidar, and particularly to utilize a appropriate algorithm for removing the interference information due to white caps and subsurface water. Wind speeds are obtained through empirical relationship with sea surface mean square slopes. Wind directions are derived from relationship between wind speeds and wind directions im plied in CMOD5n geophysical models function （GMF）. Whitecaps backscattering signals were distinguished with the help of lidar depolarization ratio measurements and rectified by whitecaps coverage equation. Subsurface water backscattering signals were corrected by means of inverse distance weighted （IDW） from neighborhood non-singular data with optimal subsurface water backscattering calibration parameters. To verify the algorithm reliably, it selected NDBC＇s TAO buoy-laying area as survey region in camparison with buoys＇ wind field data and METOP satellite ASCAT of 25 km single orbit wind field data after temporal-spa tial matching. Validation results showed that the retrieval algorithm works well in terms of root mean square error （RMSE） less than 2m/s and wind direction＇s RMSE less than 21 degree.