双激光束的非多普勒激光雷达测风系统研究

为设计一种低成本、高精度、可遥测的风速测量装置,基于米散射理论和泰勒冻结假设,提出了一种双激光束的非多普勒激光雷达测风系统。设计了以532 nm激光器为光发射单元,电荷耦合器件(charge-coupled device, CCD)为光接收单元,计算机为信息处理单元的非多普勒激光雷达测风实验装置。在风速为3m/s和2 m/s的条件下,双激光束的前向散射回波信号分别生成了左、右两束激光的光强图;对光强图进行处理,获得了气溶胶从一束激光运动到另一束激光所需的时间。结合两激光束的间距,在风速3m/s和2 m/s的条件下,获得了风速测量值,平均测量值与实际值的误差分别为7%和7.33%。非多普勒激光雷达测风系统设计简易、成本低廉,具有很强的现实意义。     

星载全极化微波散射计系统仿真与性能分析

本文对全极化微波散射计遥感海面风场的原理及其特点进行研究,建立了星载全极化微波散射计的系统仿真模型.对比了SeaWinds散射计参数下全极化与同极化的风场反演质量,结果表明全极化微波散射计在星下点以及刈幅远端的区域有良好的风场反演性能,并可提升高风速条件下的风场反演精度.最后分析了极化通道隔离度对全极化散射计系统性能的影响.     

星载GNSS-R海面风速反演方法研究

星载GNSS-R因其时延-多普勒相关功率(DDM)波形对风速的敏感性较低,采用理论与实测波形匹配的方法所得风速精度较差。针对这一问题,该文采用一种直接建立相关功率值与风速之间联系的方法获取风速信息。基于相关功率模型对散射功率进行了归一化修正及其简化处理,利用UK TDS-1卫星试验数据和散射计(ASCAT)同比观测风速分别建立归一化散射功率及其简化式与海面风速之间的经验模型。与ASCAT观测风速对比结果表明:在0~20 m/s的风速范围内,采用归一化散射功率反演风速的均方根误差为2.11 m/s;两种方法的反演精度相近,简化修正方式避免了复杂的积分运算更适用于实时处理。     

不同增益天线旋转扫描扇形波束散射计的 风场反演仿真

 本文介绍了一种星载Ku波段旋转扫描扇形波束散射计及其主要系统参数.该散射计工作在500km高度的太阳同步轨道.通过仿真比较这种散射计在不同增益天线下的风场反演能力,确定刈幅大于1000km时天线的双程增益应不小于51.5dB、距离向3dB波束宽度约为25度.最后将反演风场的质量与笔形波束的SeaWinds散射计的结果进行对比,得知这种新型的散射计有良好的风场反演能力.特别地,它能显著改善高风速条件下的风向反演精度.     

基于双法布里-珀罗干涉仪多纵模米散射多普勒激光雷达技术EI北大核心CSCD

提出了基于双法布里-珀罗干涉仪(FPI)的多纵模米散射多普勒激光雷达技术,分析了探测原理,并导出了径向风速和后向散射比测量误差公式。该技术要求多纵模激光源的纵模间隔与双FPI的自由谱间距相匹配,并将各纵模的中心频率锁定在双FPI周期性频谱曲线的交叉点附近。详细分析了频率匹配误差引起的风速测量误差。在低风速区域,由频率匹配误差造成的风速测量误差增加的百分数EV随匹配误差的增大而迅速增大;频率匹配误差不变时,EV随风速增大而缓慢减小;当频率匹配误差小于10 MHz时,EV将小于5%。设定合理的大气模式和系统参数,对基于双FPI的多纵模米散射多普勒激光雷达的探测性能进行了仿真分析。结果表明:在0~10 km高度、0~50 m/s的径向风速范围内,当距离分辨率为30 m、时间分辨率为30 s、激光发射天顶角为30°时,系统白天和晚间的径向风速测量精度分别优于1.50 m/s和1.02 m/s;在无云条件下,系统白天和晚间的后向散射比相对测量精度分别优于6.57%和4.53%。     

2 μm星载相干测风激光雷达风速及风向误差建模与分析

基于全微分和统计理论推导了星载相干测风激光雷达合成水平风速和风向误差的解析表达式,利用克拉默-拉奥误差下界代替Frehlich经验公式对风场的随机风速误差进行评估,建立了通用型的星载相干测风激光雷达合成水平风速和风向误差计算模型.在NASA/NOAA提出的星载测风激光雷达系统设计指标框架下,对风速及风向误差模型进行可行性分析,得到了总的径向随机误差随着探测距离的变化关系及水平风速区间的选取对随机误差的影响.同时,为了计算合成采样误差,改变不同的垂直分辨率和方向角取值,对水平分量的采样误差进行对比分析.仿真结果表明,合成的水平风速和风向的误差范围为0.8~3.2 m/s和2.38°~3.49°,基本符合星载测风激光雷达的相关指标要求.     

基于菲佐干涉仪的星载测风激光雷达仿真模拟

星载测风激光雷达具有高精度、高垂直分辨率、全球覆盖等特点,是获取全球风场的有效手段。聚焦于米散射通道测风模式,对冰云与气溶胶同时存在的较复杂场景实施星载激光雷达测风的仿真模拟。基于菲佐干涉仪的测风原理构建了一套包含6个子模块的正演模型,以大气激光多普勒雷达（ALADIN）仪器参数作为输入值,模拟了典型场景下的探测信号,并结合反演分析了测风精度水平。结果发现,云层和气溶胶的回波能够增强探测器获取信号的信噪比,从而提升反演精度,将风速误差控制在±1.2 m/s范围内;但当云层冰水含量较大时,由于衰减作用使得云层下方信噪比削弱,从而增大反演风速误差,部分区域甚至无法实施有效探测。另外,在采用重心法反演风速时,可通过增加累积电荷耦合器件（ACCD）探测器通道数来减小风速振荡误差。上述研究可为设计和改进星载测风技术提供参考。     

海面散射仿真中不同波浪谱和松弛率模型选取的对比研究

海面微波散射仿真对于实孔径、合成孔径雷达海洋遥感应用研究以及海洋监测雷达系统设计和信号处理都有很重要的意义,目前的海面散射仿真方法主要是基于复合表面模型来进行的,这些仿真方法都要用到海浪谱和松弛率模型。由于海浪谱、松弛率尤其是小尺度部分难以精确测量,不同的实验和拟合方法得到的模型有较大的差别,从而导致仿真海面散射结果往往相差较大,常常让人不知如何选取。该文比较了几种典型的海浪谱和松弛率模型的海面散射仿真结果与实测数据的差异,结果表明Romeiser提出的波浪谱在L-Ku波段,2-20 m/s风速范围内较其他波浪谱更好。而两种主要的松弛率模型的仿真结果差异不大。该结果可作为海面散射仿真中选取波浪谱和松弛率模型的参考。     

星载真实孔径雷达波谱仪的海浪谱反演仿真

 本文概述了星载雷达测量海浪方向谱的原理及相应的系统配置,并介绍一种端到端的系统仿真方法,分析系统在不同配置和海况下的波谱反演性能.仿真结果表明,星载雷达波谱仪能够以较高精度测量有效波高大于2.0m的风浪和中低风速条件下的涌浪(有效波高大于0.8m).最小可检测波长为40m,波长为200m时测量精度优于20m;平均之后波谱测量的方向精度优于20°.同时证明,10°入射角的波束配置优于6°入射角.     

相干激光测风雷达信号处理系统研究

介绍了基于相干探测方式的多普勒激光测风雷达的基本组成结构和原理,着重对激光测风雷达的信号处理系统进行了研究.文中给出了信号处理系统中所采用的多普勒信号处理方法,对采集到的多普勒回波信号进行了仿真研究.采用商业数据采集卡和LABVIEW软件,设计了相干激光测风雷达信号处理系统,并通过搭建的平台实现了激光视向风速的测量.通过模拟的多普勒回波信号,对信号处理系统进行了测试验证,实验结果证明了该信号处理系统能够达到0.8 m/s的速度测量精度.     

Polarimetric radar remote sensing of ocean surface wind

Experimental data are presented to support the development of a new concept for ocean wind velocity measurement (speed and direction) with the polarimetric microwave radar technology. This new concept has strong potential for improving the wind direction accuracy and extending the useful swath width by up to 30% for follow-on NASA spaceborne scatterometer mission to SeaWinds series. The key issue is whether there is a relationship between the polarization state of ocean backscatter and surface wind velocity at NASA scatterometer frequencies (13 GHz). An airborne Ku-band polarimetric scatterometer (POLSCAT) was developed for proof-of-concept measurements. A set of aircraft flights indicated repeatable wind direction signals in the POLSCAT observations of sea surfaces at 9-11 m/s wind speed. The correlation coefficients between co- and cross-polarized radar response of ocean surfaces have a peak-to-peak amplitude of about 0.4 and are shown to have an odd-symmetry with respect to the wind direction, unlike the normalized radar cross sections     

An emissivity-based wind vector retrieval algorithm for the WindSat polarimetric radiometer

The Naval Research Laboratory WindSat polarimetric radiometer was launched on January 6, 2003 and is the first fully polarimetric radiometer to be flown in space. WindSat has three fully polarimetric channels at 10.7, 18.7, and 37.0 GHz and vertically and horizontally polarized channels at 6.8 and 23.8 GHz. A first-generation wind vector retrieval algorithm for the WindSat polarimetric radiometer is developed in this study. An atmospheric clearing algorithm is used to estimate the surface emissivity from the measured WindSat brightness temperature at each channel. A specular correction factor is introduced in the radiative transfer equation to account for excess reflected atmospheric brightness, compared to the specular assumption, as a function wind speed. An empirical geophysical model function relating the surface emissivity to the wind vector is derived using coincident QuikSCAT scatterometer wind vector measurements. The confidence in the derived harmonics for the polarimetric channels is high and should be considered suitable to validate analytical surface scattering models for polarized ocean surface emission. The performance of the retrieval algorithm is assessed with comparisons to Global Data Assimilation System (GDAS) wind vector outputs. The root mean square (RMS) uncertainty of the closest wind direction ambiguity is less than 20/spl deg/ for wind speeds greater than 6 m/s and less than 15/spl deg/ at 10 m/s and greater. The retrieval skill, the percentage of retrievals in which the first-rank solution is the closest to the GDAS reference, is 75% at 7 m/s and 85% or higher above 10 m/s. The wind speed is retrieved with an RMS uncertainty of 1.5 m/s.     

Aircraft measurements of the microwave scattering signature of the ocean

Microwave scattering signatures of the ocean have been measured over a range of surface wind speeds from 3 m/s to 23.6 m/s using the AAFE RADSCAT scatterometer in an aircraft. Normalized scattering coefficients are presented for vertical and horizontal polarizations as a function of incidence angle (nadir to55deg) and radar azimuth angle (0degto360deg) relative to surface wind direction. For a given radar polarization, incidence angle, and azimuth angle relative to the wind direction, these scattering data exhibit a power law dependence on surface wind speed. The relation of the scattering coefficient to azimuth angle obtained during aircraft circles (antenna conical scans) is anisotropic and suggests that microwave scatterometers can be used to infer both wind speed and direction. These results have been used for the design of the Seasat-A Satellite Scatterometer (SASS) to be flown in 1978 on this first NASA oceanographic satellite.     

Polarimetric microwave wind radiometer model function and retrieval testing for WindSat

A geophysical model function (GMF), relating the directional response of polarimetric brightness temperatures to ocean surface winds, is developed for the WindSat multifrequency polarimetric microwave radiometer. This GMF is derived from the WindSat data and tuned with the aircraft radiometer measurements for very high winds from the Hurricane Ocean Wind Experiment in 1997. The directional signals in the aircraft polarimetric radiometer data are corroborated by coincident Ku-band scatterometer measurements for wind speeds in the range of 20-35 m/s. We applied an iterative retrieval algorithm using the polarimetric brightness temperatures from 18-, 23-, and 37-GHz channels. We find that the root-mean-square direction difference between the Global Data Assimilation System winds and the closest WindSat wind ambiguity is less than 20/spl deg/ for above 7-m/s wind speed. The retrieval analysis supports the consistency of the Windrad05 GMF with the WindSat data.     

Performance Evaluation of an Operational Spaceborne Scatterometer

Study results are presented showing performance capability of a spaceborne scatterometer to measure ocean surface wind speed and direction operationally. In addition, a research mode is described which will allow development of improved radar signatures for ocean, sea ice, and land targets. The study results show that a scatterometer can meet the operational user requirement of ±2 m/s wind-speed accuracy (or ±10 percent, whichever is greater) and ±20° wind-direction accuracy over most of the expected ocean surface conditions. The six-beam scatterometer design evaluated is shown to be skillful (>90 percent correct) in specifying the correct wind-vector solution (with a 1800° ambiguity) from the multiple solutions derived. Further improvement must rely on meteorological and pattern-recognition techniques which are being studied.     

Direction interval retrieval with thresholded nudging: a method forimproving the accuracy of QuikSCAT winds

The SeaWinds scatterometer was developed by NASA JPL, Pasadena, CA, to measure the speed and direction of ocean surface winds. It was then launched onboard the QuikSCAT spacecraft. The accuracy of the majority of the swath and the size of the swath are such that the SeaWinds on QuikSCAT Mission (QSCAT) meets its science requirements despite shortcomings at certain cross-track positions. Nonetheless, it is desirable to modify the baseline processing in order to improve the quality of the less accurate portions of the swath, in particular near the far swath and nadir. Two disparate problems have been identified for these regions. At far swath, ambiguity removal skill is degraded due to the absence of inner beam measurements, limited azimuth diversity and boundary effects. Near nadir, due to nonoptimal measurement geometry, (measurement azimuths approximately 180° apart) there is a marked decrease in directional accuracy even when ambiguity removal works correctly. Two algorithms have been developed: direction interval retrieval (DIR) to address the nadir performance issue and thresholded nudging (TN) to improve ambiguity removal at far swath. The authors illustrate the impact of the two techniques by exhibiting prelaunch simulation results and postlaunch statistical performance metrics with respect to ECMWF wind fields and buoy data     

An L-band geophysical model function for SAR wind retrieval using JERS-1 SAR

An L-band geophysical model function is developed using Japanese Earth Resources Satellite-1 (JERS-1) synthetic aperture radar (SAR) data. First, we estimate the SAR system noise, which has been a serious problem peculiar to the JERS-1 SAR. It is found that the system noise has a feature common in all the SAR images and that the azimuth-averaged profile of noise can be expressed as a parabolic function of range. By subtracting the estimated noise from the SAR images, we can extract the relatively calibrated ocean signals. Second, using the noise-removed SAR data and wind vector data from the NASA Scatterometer and buoys operated by the Japan Meteorological Agency, we generate a match-up dataset, which consists of the SAR sigma-0, the incidence angle, the surface wind speed, and wind direction. Third, we investigate the sigma-0 dependence on incidence angle, wind speed, and wind direction. While the incidence angle dependence is negligible in the present results, we can derive distinct sigma-0 dependence on wind speed and direction. For wind speeds below 8 m/s, the wind direction dependence is not significant. However, for higher wind speeds, the upwind-downwind asymmetry becomes very large. Finally, taking into account these characteristics, a new L-band-HH geophysical model function is produced for the SAR wind retrieval using a third-order harmonics formula. Resultant estimates of SAR-derived wind speed have an rms error of 2.09 m/s with a negligible bias against the truth wind speed. This result enables us to convert JERS-1 SAR images into the reliable wind-speed maps.     

A systematic comparison of QuikSCAT and SAR ocean surface wind speeds

We performed a systematic comparison of wind speed measurements from the SeaWinds QuikSCAT scatterometer and wind speeds computed from RADARSAT-1 synthetic aperture radar (SAR) normalized radar cross section measurements. These comparisons were made over in the Gulf of Alaska and extended over a two-year period, 2000 and 2001. The SAR wind speed estimates require a wind direction to initialize the retrieval. Here, we first used wind directions from the Navy Operational Global Atmospheric Prediction System (NOGAPS) model. For these retrievals, the standard deviation between the resulting SAR and QuikSCAT wind speed measurements was 1.78 m/s. When we used the QuikSCAT-measured wind directions to initialize the inversion, comparisons improve to a standard deviation of 1.36 m/s. We used these SAR-scatterometer comparisons to generate a new C-band horizontal polarization model function. With this new model function, the wind speed inversion improves to a standard deviation of 1.24 m/s with no mean bias. These results strongly suggest that SAR and QuikSCAT measurements can be combined to make better high-resolution wind measurements than either instrument could alone in coastal areas.     

Ocean surface wind speed and direction retrievals from the SSM/I

A semiempirical model is developed that retrieves ocean surface wind direction information in addition to improved wind speeds from Special Sensor Microwave/Imager (SSM/I) measurements. Radiative transfer and neural network techniques were combined in the authors' approach. The model was trained and tested using clear sky cases, but atmospheric transmittance is retrieved so that retrieval in other than clear sky conditions is possible. With two SSM/I instruments currently providing operational ocean surface wind speed retrievals, the addition of wind direction information and improved wind speed retrievals will enhance the impact of this data in weather prediction models and marine weather forecasting     

Validating a scatterometer wind algorithm for ERS-1 SAR

The ocean surface wind field is observed from space operationally using scatterometry. The European Space Agency's (ESAs) ERS-1 satellite scatterometer routinely produces a wind product that is assimilated into forecast models. Scatterometry cannot give accurate wind estimates close to land, however, because the field of view of a spaceborne scatterometer is on the order of 50 km. Side lobe contamination, due to the large contrast in backscatter between land and water, compounds the problem. Synthetic aperture radar (SAR) can provide wind speed and direction estimates on a finer scale, so that high-resolution wind fields can be constructed near shore. An algorithm has been developed that uses the spectral expression of wind in SAR imagery to estimate wind direction and calibrated backscatter to estimate wind strength. Three versions, based on C-band scatterometer algorithms, are evaluated for accuracy in potential operational use. Algorithm estimates are compared with wind measurements from buoys in the Gulf of Alaska, Bering Strait, and off the Pacific Northwest coast by using a data set of 61 near-coincident buoy and ERS-1 SAR observations. Representative figures for the accuracy of the algorithm are ±2 m/s for wind speed and ±37° for wind direction at a 25-km spatial resolution