卫星遥感反演海面风矢量数据真实性检验与质量评价

微波散射计(QuikSCAT)和微波辐射计(WindSAT)的10m风矢量数据是目前覆盖范围最大、持续时间最长的海面风场卫星数据产品。利用QuikSCAT和WindSAT运行时间重叠的风矢量原始轨道资料,分别与同步的全球海洋浮标实测风矢量资料进行比较。结果显示:QuikSCAT平均风速略大于浮标,平均绝对误差约为1m/s;风向平均绝对误差在8°以下。WindSAT平均风速略大于浮标,平均绝对误差不超过0.5m/s,均方根误差约为1m/s;风向平均绝对误差在10°以下。在全球海域,QuikSCAT和WindSAT风矢量数据质量高,可信度极好。QuikSCAT和WindSAT在同一点上空的过境时间为准同步,并且二者的风矢量数据相关性极好,可以互相替代。     

SMAP卫星后向散射系数与海表面风场关系

海面风是海气互相作用的重要参数之一,如何通过雷达后向散射数据有效提取海表面风场信息,对于海洋动力环境遥感监测具有重要的研究意义。使用SMAP卫星L波段真实孔径雷达数据和国家环境预测中心再分析风场数据进行匹配,利用地球物理模型函数分析了SMAP卫星数据的后向散射系数与海表面风场之间的关系,讨论了不同风速和不同相对风向角时SMAP卫星数据反演海表面风场的潜力。研究显示,水平极化和垂直极化的后向散射系数与风速的关系紧密,适于海表面风场的反演;SMAP卫星数据存在正-侧风不对称现象和逆正-侧风不对称现象;在相对风向角为90°和270°时后向散射系数与风场的关系较为模糊;随着风速的增加,后向散射系数与相对风向角的规律关系也越来越明显,振幅也随风速增大而增大。GMF函数计算的风速偏差为1.19m/s(水平极化)和1.51m/s(垂直极化),均方根误差为1.58m/s(水平极化)和1.67m/s(垂直极化)。     

SMAP卫星后向散射系数与海表面风场关系

海面风是海气互相作用的重要参数之一,如何通过雷达后向散射数据有效提取海表面风场信息,对于海洋动力环境遥感监测具有重要的研究意义。使用SMAP卫星L波段真实孔径雷达数据和国家环境预测中心再分析风场数据进行匹配,利用地球物理模型函数分析了SMAP卫星数据的后向散射系数与海表面风场之间的关系, 讨论了不同风速和不同相对风向角时SMAP卫星数据反演海表面风场的潜力。研究显示,水平极化和垂直极化的后向散射系数与风速的关系紧密,适于海表面风场的反演;SMAP卫星数据存在正-侧风不对称现象和逆正-侧风不对称现象;在相对风向角为90°和270°时后向散射系数与风场的关系较为模糊;随着风速的增加,后向散射系数与相对风向角的规律关系也越来越明显,振幅也随风速增大而增大。GMF函数计算的风速偏差为1.19 m/s（水平极化）和1.51 m/s（垂直极化）,均方根误差为1.58 m/s（水平极化）和1.67 m/s（垂直极化）。     

非降水条件下微波辐射计海面风遥感产品性能分析

通过对5种微波辐射计SSM/I、SSM/IS、TMI、AMSR\|E和WINDSAT以及2种微波散射计ASCAT和QUIKSCAT多年的海面风产品同浮标同步的实测资料进行数据匹配处理,再对匹配后的数据进行数据分析和统计。研究结果表明:微波辐射计遥感海面风的性能在1 m/s左右,可以满足绝大多数应用的需求。微波辐射计的低频海面风产品性能优于中频产品,但是中频数据地面分辨率高,建议在近海应用中使用中频产品,在大洋应用中使用低频产品。就不同微波辐射计而言,AMSR\|E和WINDSAT性能较优,SSM/I和SSM/IS性能较差,TMI则处于中等水平。微波辐射计测量风速的性能与微波散射计相比处于同一水平,但在高风速段微波辐射计有一定优势。微波辐射计中仅全极化微波辐射计WINDSAT具有测量海面风向的能力,在低风速段,WINDSAT测量海面风向的性能远远不及微波散射计,只有风速超过6 m/s时,WINDSAT提供的海面风向数据才能符合应用的需求。当风速超过8 m/s后,WINDSAT遥感海面风向的能力就和微波散射计基本一致。在此基础上,提出了强风条件下深入研究的必要性,并对浮标测风存在的问题做出了初步的分析并指出了改进的方向。
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星载SAR在海气边界层气象学中的应用研究

SAR(Synthetic Aperture Radar,合成孔径雷达)作为一种现代高空间分辨率成像侧视雷达,对地球表面海洋所成的图像中蕴含了极为丰富的中尺度及亚中尺度海洋大气边界层的信息,因此对边界层气象学研究有着非常重要的意义。但是,使用SAR研究海气边界层这一涵盖微波遥感、气象学及海洋学等学科的科学前沿课题在国内却少有文献报道。在此背景下,首先介绍了SAR反演海洋大气边界层的研究概况,回顾了SAR反演海气边界层参数的原理和方法。然后以2002年5月7日当地时间10时53分ERS-2卫星获取的香港地区(22.097°N,E 114.300°E)SAR海洋图像为例,进行了反演风向风速的初步试验,最终获得了较高精度的风矢量。具体过程如下:先对SAR图像进行预处理,包括ADC(Analog Digital Converter,模数转换器)补偿、精确校准及斑点滤波等过程;然后利用经典的谱分析方法求得具有180°模糊度的风向,再用香港天文台气象浮标实测资料消除这一不确定性得到了真实的相对风向;紧接着利用CMOD4地球物理模式函数计算得到了海面上10 m高的风速。与气象浮标站所记录的平均风速和风向比较,两个20 km×20 km大小的试验区域求得的风向误差分别为23.71°和7.00°,平均风速误差分别为0.18 m/s和-0.12 m/s。结果表明,如果对SAR预先进行严格的预处理,结合经典的谱分析方法和CMOD4模型,即可获取高精度的风矢量。这一结果为今后海洋大气边界层的研究奠定了良好的基础。     

矿用对射式风速风向传感器设计

针对目前风速传感器启动风速高、设计方案复杂、无法准确测量巷道整个断面平均风速的问题,基于超声波对射式测风原理,设计了以STM32为核心的矿用对射式风速风向传感器,介绍了传感器总体结构、收发电路设计、滤波算法及软件流程。该传感器改变了以点带面的测风方式,通过大距离（5～12 m）超声测风技术测量巷道中线风速,以该风速代表整个巷道的平均风速,提高了巷道风速测量的准确性和实时性。依据设计方案研发了测试样机,在环形风洞中的测试结果表明,该传感器测量值与风速标准值在0.1～15 m/s内具有较好的一致性,测量误差小于0.1 m/s,能够满足智能化矿井对巷道风速测量精度的要求。     

基于激光雷达测风仪的风电机组偏航优化控制方法

为了能通过短时安装激光雷达测风仪对传统的风向标测得偏航误差进行修正,提高风电机组的对风精度,根据激光雷达测风仪的风向检测原理和实测数据的分析,总结了叶轮前方风向变化的规律,确定了要检测到不受机组干扰的风向的最佳检测距离和检测光束的锥角;经过不同风速段的偏航误差特点分析,采用了分高、低两个风速段,用最小二乘法去拟合从本地...     

基于GNSS-R的反演海面风速技术的研究

对利用全球导航定位系统的海洋反射信号(GNSS-R)反演海面风速的方法进行了研究。GNSS-R技术作为一种新型的、低成本的海洋微波遥感测风技术,与其他测风技术相辅相成,弥补了某些测风手段的不足。文中还讨论了散射信号相关功率模型中的散射截面、多普勒区、等延时区、天线覆盖区四部分函数的定义和性质。使用Elfouhaily海浪谱模型,数值模拟了机载高度下散射信号相关功率的理论波形,在此基础上,又结合机载高度下获得的实测数据反演得到海面风速,反演得到的风速的均值与试验时浮标数据所对应的风速的均值比较相差1.4 m/s,误差在可接受的范围内,反演得到的风速与浮标数据相一致。     

海上风电场不同高度层风速折算系数及高层风速变化特征研究

针对目前国内外缺乏对风电场微观区域风速随高度订正模型以及对风机实际高度处风速变化特征研究的现状,本文利用多层结构测风塔一个完整年观测数据以及1980年—2020年10m高气象观测站资料,建立不同高度层风速随10m风速折算系数,并将近40年10m高气象观测站资料反演至70m高度,采用线性拟合、核密度估计、M-K突变检验、小波变换等方法,研究70m高层风速演变规律。研究表明：不同高度层风速与10m风速均存在较强相关性关系,30m、60m、70m风速随10m风速折算系数分别为0.41、0.23、0.21;70m风速每10年下降0.14m/s,且对核密度估计窗口宽度、核函数选取进行比选,显示年份平均风速集中在5m/s～8m/s之间;1991年、2003年为70m风速两个突变年份;存在3年至5年小尺度、20年中尺度、20年至32年长尺度变换周期,并进一步验证风速突变年份。     

基于GPS、磁罗盘与大气数据计算机的无人机风估计

针对无人机实时航路规划及适应环境变化的自主能力发展需求,提出了一种新的风估计与空速校准的方法;该方法基于GPS接收机、大气计算机和磁罗盘等传感器实现;针对定常风模型,风速、风向能够利用地速、风速和空速之间的速度矢量三角形关系计算得到;采用无导扩展卡尔曼滤波(DEKF),估计风场信息以及真空速的比例校准系数;利用某型无人机数字仿真平台,在2D定常风条件下进行了全过程自主飞行仿真;仿真结果表明:该方法在航路跟随的直线段、转弯段均能准确估计。     

Synergistic application of oceanographic variables from multi-satellite sensors for forecasting potential fishing zones: methodology and validation results

This study uses synergistic application of satellite-derived chlorophyll concentration (CC), sea surface temperature (SST) and sea surface wind (SSW) for forecasting potential fishing zones (PFZs). PFZs are validated in near-real time through fishing operations and detailed statistical analysis of fishing operation data. CC and SST images were derived from Indian Remote Sensing Satellite-Ocean Colour Monitor (IRS-OCM) and NOAA-AVHRR, respectively, to delineate the oceanographic features exhibiting different oceanic processes. QuikSCAT/SeaWinds derived sea surface wind vectors were used to understand, quantify and demonstrate the variability of wind-induced water mass flow as well as their impacts on features/oceanographic process. Oceanographic features such as eddies, rings and fronts were found to be shifted according to the speed and direction of the wind. An algorithm was developed to compute water mass transport and feature shift. An improved methodology was developed and demonstrated using these prime variables, which are responsible for fishery resources distribution. PFZ forecasts were generated and validated through near-real-time fishing operations. The fishing operations data were taken from the logbooks of fishing vessels for detailed statistical analysis. On average, 80% of observations were recorded with more yield than monthly mean catch in the respective areas. A paired t-test showed statistically significant results.     

一种基于海面背景的WindSat极化通道替代定标方法研究

凭借能够测量海面辐射全部4个Stokes参数的能力,全极化微波辐射计成为测量海面风场的一种新手段。由于风向信号仅仅只有几K甚至更小的振幅,故对全极化微波辐射计极化通道的定标精度要求颇高。使用WindSat实测亮温及其海面参数匹配数据集,基于海面风向信号的谐波特征,对WindSat极化通道亮温的定标偏差和仪器噪声进行了计算和分析。研究结果表明:WindSat的10.7、18.7和37.0 GHz这3个频率的极化通道均存在明显的定标偏差,并且该定标偏差会随着升轨和降轨、前向刈幅和后向刈幅等不同观测模态发生变化。另外,WindSat极化通道的仪器噪声较小,其中T3的仪器噪声在0.2 K以内,T4的仪器噪声在0.1 K以内。     

Comparison of wind data from QuikSCAT and buoys in the Indian Ocean

The performance of QuikSCAT‐derived wind vectors is evaluated using in‐situ data from moored buoys over the Indian Ocean. The results show that the mean differences for wind speed and wind direction are 0.37 ms^?1 and 5.8°, root mean square deviations are 1.57 ms^?1 and 44.1° and corresponding coefficients of correlation are 0.87 and 0.75, respectively. The matching between in‐situ and satellite estimates seems to be better in the North Indian Ocean than in the Equatorial Indian Ocean. The effects of sea surface temperature and air–sea temperature difference on wind residuals were also investigated. In general, QuikSCAT is found to overestimate the winds. It is speculated that low wind speed during rain‐free conditions and high wind speed, normally associated with rain, may be the reason for the less accurate estimation of the wind vector from QuikSCAT over the Indian Ocean.     

Assessment of the offshore wind resource in Japan with the ASCAT microwave scatterometer

We analysed wind speed and direction off the coast of Japan using data from the satellite-borne Advanced Scatterometer (ASCAT) and the Weather Research and Forecasting model (WRF), validated these data using in situ wind measurements from 20 buoys, and evaluated the effect of the long time intervals from ASCAT observations on wind resource assessment. More than 25 km from the coast, and at heights of 10 m, the ASCAT wind speed has negative biases of up to 3.4% and root mean square errors of up to 18.5%; its wind direction has 11° to 27° of mean absolute error compared to buoy measurements at a height of 10 m. These accuracies are better than either the expected accuracies reported in the technical manual or those simulated with WRF with its spatial resolution of 10 km. We also evaluated long-term average ASCAT wind speeds in comparison to 4- and 5-year averages of in situ buoy wind speeds measured at three buoys, with resulting differences of –0.3%, –6.3%, and – 1.6%. Furthermore, wind roses show that appearance frequencies of the ASCAT wind direction for the long term are in a good agreement with those of the measurements at the three buoys. Our results show that the ASCAT-derived wind speed and direction are appropriate more than 25 km from the coast, and that the long time interval between ASCAT observations has an insignificant effect on wind resource assessment, if at least 4 or 5 years of averaged ASCAT data are used.     

An algorithm for the retrieval of sea surface wind fields using X-band TerraSAR-X data

TerraSAR-X (TS-X) is a new, fully polarized X-band synthetic aperture radar (SAR) satellite, which is a successor of the Spaceborne Imaging Radar X-band Synthetic Aperture Radar (SIR-X-SAR) and the SRTM. TS-X has provided high-quality image products over land and oceans for scientific and commercial users since its launch in June 2007. In this article, a new geophysical model function (GMF) is presented to retrieve sea surface wind speeds at a height of 10 m (U ₁₀) based on TS-X data obtained with VV polarization in the ScanSAR, StripMap and Spotlight modes. The X-band GMF was validated by comparing the retrieved wind speeds from the TS-X data with in situ observations, the high-resolution limited area model (HIRLAM) and QuikSCAT scatterometer measurements. The bias and root mean square (RMS) values were 0.03 and 2.33 m s^?1, respectively, when compared with the co-located wind measurements derived from QuikSCAT. To apply the newly developed GMF to the TS-X data obtained in HH polarization, we analysed the C-band SAR polarization models and extended them to the X-band SAR data. The sea surface wind speeds were retrieved using the X-band GMF from pairs of TS-X images obtained in dual-polarization mode (i.e. VV and HH). The retrieved results were also validated by comparing with QuikSCAT measurements and the results of the German Weather Service (DWD) atmospheric model. The obtained RMS was 2.50 m s^?1 when compared with the co-located wind measurements derived from the QuikSCAT, and the absolute error was 2.24 m s^?1 when compared with DWD results.     

Comparison of Oceansat-2 scatterometer- to buoy-recorded winds and spatial distribution over the Arabian Sea during the monsoon period

For this wind resource assessment (WRA) study, wind speed and direction are the fundamental inputs. Also, these studies are data driven and require large historical wind speed data sets available on the site. This work explores the application of space-based scatterometer winds for assimilation into WRA studies towards the development of offshore wind energy. This article focuses on estimating the performance of Oceansat-2 scatterometer (OSCAT)-derived wind vector using in situ data from buoys at different locations in the Arabian Sea. A comparative study between three methods for estimating the equivalent neutral winds (ENW) for buoys is carried out. OSCAT winds were closest to ENW estimated by the Liu–Katsaros–Businger (LKB) method. The spatial and temporal windows for comparison were 0.5° and ±60 minutes, respectively. The monsoon months (June–September) of 2011 were selected for study. The root mean square deviation for wind speed is less than 2.5 m s^?1 and wind direction is less than 20°, and a small positive bias is observed in the OSCAT wind values. From the analysis, the OSCAT wind values are consistent with in situ-observed values. Furthermore, wind atlas maps were developed with OSCAT winds, representing the spatial distribution of winds at a height of 10 m over the Arabian Sea.     

Regional accuracy of QuikSCAT gridded winds

The quality of gridded 00 UTC and 12 UTC QuikSCAT wind speed fields provided by the Florida State University (FSU) and NASA Jet Propulsion Laboratory (JPL) are analysed over the Bay of Bengal during May–August 2001. Additionally, an examination of these fields is performed over the Gulf of Mexico for the May–August period from 2001 to 2003. Both 00 UTC and 12 UTC time almost coincide with QuikSCAT sampling times (twice a day) and correspond to either early morning or late evening local time over these regions. The primary restriction for generating accurate maps with a temporal resolution of 12 hours and less is a lack of adequate sampling. Due to non‐uniform spatial‐temporal sampling of the scatterometer, interpolation procedures cannot provide proper estimates in data gaps over the regions not covered by a swath. Wind speed autocorrelation coefficients for gridded datasets have been compared with that of original QuikSCAT data and buoy winds. It is shown that the spatial and temporal interpolation used to obtain these datasets results in smoothing of the QuikSCAT wind speeds, reducing their original variance. This smoothing is amplified where substantial diurnal wind variability occurs. A comparison with buoy data shows that FSU and JPL gridded fields are unable to reproduce correctly observed low correlations in wind speed between morning and evening breeze flows and diurnal variability of winds, leading to a reduced difference between 00 UTC and 12 UTC values in comparison with buoys and swath QuikSCAT data. Rather, the FSU and JPL maps describe daily mean fields. Another consequence of the spatial‐temporal interpolation is that the winds are distorted at a frequency matching the dominant sampling interval (3–4 days) of QuikSCAT measurements over the Bay of Bengal.     

Use of an optimum interpolation method to construct a high-resolution global ocean surface vector wind dataset from active scatterometers and passive radiometers

This study presents a new 0.25° gridded 6-hourly global ocean surface wind vector dataset from 2000 to 2015 produced by blending satellite wind retrievals from five active scatterometers (QuikSCAT, ASCAT-A, ASCAT-B, OSCAT, and HY-2A), nine passive radiometers (four SSM/I sensors, two SSMIS sensors, TMI, AMSR-E, and AMSR2) and one polarimetric radiometer (WindSat) with reanalysis from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) employing an optimum interpolation method (OIM). The accuracy of this wind product is determined through various comparisons with buoy measurements, NCEP/NCAR reanalysis and the cross-calibrated multi-platform (CCMP) winds. The comparisons indicate that OIM winds agree well with buoys, showing a root-mean-squared difference of 1.32 m s^?1 for wind speed and 24.73° for wind direction over 0–30 m s^?1 wind speed range. And the quality of OIM winds is improved significantly relative to NCEP/NCAR reanalysis and can be comparable with CCMP winds. Furthermore, OIM winds can reveal abundant small-scale features that are not visible in reanalysis data. In addition, the wind speed and direction retrievals of most satellites are proved to play an important role in generating the high-quality product, but the procedure for including HY-2A winds and WindSat wind directions should be further explored.