船测资料与智利外海QuikSCAT风场比较分析

利用大洋渔船在智利外海观测的风场资料与QuikSCAT 10 m散射风原始轨道资料L3产品进行了比较分析。两种资料的偏差统计特征显示:①智利外海船测风速总体上高于QuikSCAT风速,船测风向总体上偏于QuikSCAT风向的左侧;②智利外海船测风场资料与QuikSCAT散射风的风速偏差集中分布在-1~1 m/s之间;风向偏差主要集中分布于-60°~-10°之间,其次为10°~60° 和-10°~10°段;③智利外海白天的风速偏差特征值均小于夜晚,昼、夜风向平均偏差数值差别很大,但昼、夜风向平均绝对偏差、均方根偏差数值相差不大;④2008年智利外海船测风场资料与QuikSCAT散射风的偏差大于其他年份的整体平均值,在高风速段风速偏差尤为明显。     

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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基于FY-3B微波成像仪的海面风速反演

风云三号B星微波成像仪的10.65、18.7、23.8和36.5 GHz频点对海表面粗糙度和介电特性比较敏感,能够用于海面地球物理参数的反演。为获得一种适用于全球大部分海域的海面风速反演算法,利用快速辐射传输模式和再分析大气廓线库模拟微波成像仪海面微波辐射特性,在此基础上建立了半经验反演算式,并利用浮标现场测量数据及WindSat全极化辐射计风速产品对半经验算法和经验算法分别进行了验证和对比。另外,通过分析风向对风速反演的影响,借助AT BV-TBH模型,尝试利用查找表算法对风向造成的晴空区域风速反演偏差进行初步校正。校正风向误差后,反演风速与浮标风速的均方根误差为0.9775 m/s。     

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。     

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）。     

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)。     

煤矿用超声波式风速风向传感器设计

为了监测煤矿瓦斯突出事故导致的瓦斯逆流情况,应用超声波时差法原理设计了煤矿用超声波式风速风向传感器。该传感器利用超声波发射、接收的时差与风速、风向的关系,经过运算得到被测风向和风速。测试结果表明,该传感器稳定性强,测量精度高,风速测量范围为0.4~15m/s,误差不超过0.3m/s,风向测量范围为0~360°,误差不超过3°。     

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。     

基于海洋气象漂流浮标的动态测风技术研究

利用海洋气象漂流浮标对海洋风数据进行观测具有成本低、可抛弃性的优势,然而,海洋气象漂流浮标在海上动态观测环境中传感器倾角不断变化且改变速度不确定,引起了较大的测量误差;针对这一问题,搭建了模拟海洋动态环境的测风实验平台,选用FT742-SM型超声波测风传感器对风速风向数据进行测量,并利用欧拉角模型和四元数模型对测风传感器姿态变化时的三个倾角进行解算;通过多次实验数据对比分析,发现传感器俯仰角θ和横滚角γ对风速风向测量影响最大,进而提出了多变量拟合的方法对所测风速风向数据进行误差补偿,补偿后的数据准确性得到了较大的提升;最后,结合真风订正算法设计了漂流浮标测风算法总流程,为后续的海洋气象漂流浮标测风提供了很好的参考价值。     

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。     

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.     

Research on Measurement Coverage and Uncertainty Analysis of GPM Microwave Imager SST Products

Though the global precipitation measurement microwave imager(GMI)has been a new microwave sensor for about two years,no capability evaluation of GMI SST has been made.For providing some helpful information to using the products later,in the paper,monthly/annual GMI SST measurement coverage are calculated using GMI products during 4/2014 and 3/2016 and spatial and temporal variation of the coverage are also analyzed.Besides,to generate matchups set with buoy and Voluntary Observing Ship Climate(VOSClim)measurements as in\|situ data strategy has been made,and retrieval uncertainty of GMI SST are finally evaluated.All the work show,(1)the GMI SST annual coverage is about 0.51,smaller than global average,which is 0.59,but it’s significant bigger than Infrared SSTs;(2)using±3 h/0.1° as temporal and spatial match windows,and 6.5 m/s as a wind speed threshold to exclude outliers can eliminate some of the errors effectively;(3)GMI SST Bias is-0.02±0.89 ℃,and is approximate to bias of some other SST products.     

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.     

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.     

A satellite-derived climatology of global ocean winds

An interactive Climatology of Global Ocean Winds (COGOW) is presented based on 5 years (August 1999-July 2004) of QuikSCAT satellite measurements of wind speed and direction 10 m above the sea surface. This climatology provides the first high spatial resolution, observationally based, online atlas of ocean winds. Users can retrieve climatological wind maps and wind statistics, both in tabular and graphical form, from the COGOW web-based atlas. The global coverage of these data provides highly accurate information about the wind statistics in regions of the world ocean that are sparsely sampled by ships and buoys. A case study of the recovery of the vessel Ehime Maru off the Hawaiian Island of Oahu is presented to demonstrate the usage and value of COGOW. Evidence of air-sea interactions, one of many wind phenomena visible within COGOW, is discussed to further familiarize users with COGOW. Finally, the utility of COGOW with regard to various operational and research communities is summarized.     

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.