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.     

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.     

一种基于灰度互相关法的扫描模式下的风场反演算法

该文分析了传统风场反演算法的理论依据、特点和不足,提出一种新的扫描模式下的风场反演算法。该算法考虑两幅相邻扫描周期所成合成孔径雷达图像中海浪波纹的相关特性,应用互相关方法,求解波纹运动矢量,确定风场风向,然后代入地球物理模型求解风速。与传统风场反演算法及浮标实测数据对比可知,该算法提高了风场反演的精确度,且不存在风向模糊问题。机载雷达实测数据的处理结果证明了方法的有效性。     

利用浮标和NWP风场对HY-2散射计联合定标验证

本文对海洋二号卫星微波散射计(Haiyang-2 Scatterometer,HY-2 SCAT)进行了海洋定标算法研究,并使用数值天气预报模型风场(Numerical Weather Prediction,NWP)和浮标数据对定标后反演风场进行联合验证.通过匹配2012年12月份的HY-2 SCAT反演风场、NWP风场及浮标的观测数据,共得到无降雨条件下的3112个25km分辨率的匹配数据.对匹配数据进行分析时,采用基于变量的误差分析方法能够得到比传统线性回归方法更精确的验证结果.选取在风场U、V分量进行联合验证能得到较在风速、风向上更为有利的验证结果.验证结果表明,经过海洋定标法之后的HY-2 SCAT测量后向散射系数的误差残余小于0.15dB,其反演风场与浮标及NWP数据相吻合,U、V分量相对浮标及NWP数据偏差均小于0.23m/s,验证了该定标算法的有效性及定标后反演风场的高精度.     

一种新的海洋风场矢量估计算法

基于合成孔径雷达(SAR)图像的海面风场估计已经得到广泛认可。多数风速反演算法是以估计的风向、校正的δvv为先验条件,应用海风模型计算而得的。在相同风向的情况下,应用不同的海风模型会得到不同的风速反演值,因此选择合适的模型是风场估计的关键。同时,风向数据的精确度也很重要,即使不大的误差也会给风速的反演结果带来明显偏差。为解决上述问题这里提出一种不需要预先已知风向数据的风场估计算法。该算法将基于海洋SAR图像中风浪的条纹信息,以及风浪条纹生成的自相关函数的周期性估计风速数据,同时由风浪条纹的最短周期方向估计风向数据,从而估计出完整的风场矢量。仿真结果显示,该算法对风速和风向数据有较高的估计精度。     

基于随机平均梯度下降和对比源反演的非线性逆散射算法研究

采用非线性对比源反演(CSI)算法求解电磁逆散射问题时,在每次迭代过程中都涉及到求解散射场数据关于对比源和总场的微分,即Jacobi矩阵,该矩阵求解导致算法存在计算代价大和收敛速度慢等问题。该文在CSI框架下,采用一种基于随机平均梯度下降的对比源反演算法(SAG-CSI)代替原来的全梯度交替共轭梯度算法来重构介质目标介电常数的空间分布信息。该方法在每次迭代中只需计算随机抽取的部分测量数据在目标函数中的梯度信息,同时目标函数对未抽中的测量数据的梯度信息保持不变,用以上两部分梯度信息共同求解出目标函数的最优值。由模拟数据结果表明,该方法与传统CSI方法在成像精度相比拟的情况下,降低了计算代价并提高算法收敛速度。     

基于太阳辐射计的气溶胶光学特性反演算法研究

介绍了一种利用太阳直接辐射和天空漫射辐射来反演气溶胶光学特性的方法,研究了其算法实现过程.结合太阳辐射计CE318所测得的太阳直射辐照度和天空漫射辐亮度数据,反演获得了晴朗天气下四个波段的气溶胶光学厚度、Angstrom参数、单次散射反照率、散射相函数和偏振相函数.算法的实现为解决光学遥感应用中气溶胶散射相函数和偏振相函数的测量问题提供了一个实用的方法.     

基于Gabor小波变换的机载SAR海面风向反演方法

给出了一种基于Gabor小波变换的机载合成孔径雷达（SAR）海面风向反演的新方法。该方法利用Gabor小波对SAR图像进行二次小波分解,并对小波系数作FFT变换来获取图像谱,其低波数谱连线的垂线方向就是海面风场的风向。利用该方法获得了SAR图像海面风向信息,并与àtrous算法反演结果、浮标测得的海面风向（真值）进行了比较。实验结果表明,采用该方法获得的机载SAR海面风向反演结果与海面浮标实测数据吻合,比àtrous算法有较大改进。     

小角前向散射偏振比法颗粒粒度分布反演

在光散射法的颗粒粒径测量方法中,传统测量模型的测量结果易受光路中颗粒杂质的影响。在Mie散射理论的基础上,将小角前向散射法与偏振比法相结合,推导了新的散射光能与粒径分布关系式,构造了传统小角前向散射法和小角前向散射偏振比法两种目标函数,并引入一种非独立模式算法人工鱼群算法对两种方法的目标函数进行反演。仿真采用服从Johnson-SB单峰分布均匀球形颗粒,分别对两种目标函数散射光能加入5%,10%,15%的随机噪声。仿真结果显示,利用人工鱼群算法对小角前向散射偏振比法目标函数反演得到的反演精度、抗噪声能力和鲁棒性都明显优于传统小角前向散射法目标函数的反演结果。     

相干多普勒激光雷达风场反演方法研究与实验印证

提出基于共轭梯度算法的速度方位显示(VAD)风场反演方法,应用最优化理论,将共轭梯度算法代替传统VAD方法中的傅里叶级数展开来求取最优解,并针对算法在风场反演应用时存在不收敛于最优解的问题,使用Hessian矩阵对算法进行了修正。同时开展了多普勒激光雷达与符合IEC 61400-12-1国际标准的高精度风杯风速计的43天对比实验,结果显示,当激光雷达的方位角扫描范围为60°、径向个数为7个时,两者的风速、风向相关系数分别为0.991和0.998,风速、风向标准偏差分别为0.52m/s和5.1°,风速、风向偏差分别为-0.02m/s和3.6°。对比实验结果表明,基于共轭梯度算法的VAD风场反演方法使用较小的扫描方位角仍能保证其测量的准确性满足国际标准,具有更强的适用性,同时印证了激光雷达系统的测量性能,为动态复杂风场的监测提供了更佳的选择。     

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 advanced ambiguity selection algorithm for SeaWinds

SeaWinds on QuikSCAT, a spaceborne Ku-band scatterometer, estimates ocean winds via the relationship between the normalized radar backscatter and the vector wind. Scatterometer wind retrieval generates several possible wind vector solutions or ambiguities at each resolution cell, requiring a separate ambiguity selection step to give a unique solution. In processing SeaWinds on QuikSCAT data, the ambiguity selection is "nudged" or initialized using numerical weather prediction winds. We describe a sophisticated new ambiguity selection approach developed at Brigham Young University (BYU) that does not require nudging. The BYU method utilizes a low-order data-driven Karhunen-Loeve wind field model to promote self-consistency. Ambiguity selected winds from the BYU method and standard SeaWinds processing are compared over a set of 102 revs. A manual examination of the data suggests that the nonnudging BYU method selects a more self-consistent wind field in the absence of cyclonic storms. Over a set of cyclonic storm regions, BYU performs better in 9% of the cases and worse in 20% of the cases. Overall, the BYU algorithm selects 93% of the same ambiguities as the standard dataset. This comparison helps validate both nonnudging and nudging techniques and indicates that SeaWinds ambiguity selection can be generally accomplished without nudging.     

Evaluation of high-resolution ocean surface vector winds measured by QuikSCAT scatterometer in coastal regions

The SeaWinds scatterometer onboard QuikSCAT covers approximately 90% of the global ocean under clear and cloudy condition in 24 h, and the standard data product has 25-km spatial resolution. Such spatial resolution is not sufficient to resolve small-scale processes, especially in coastal oceans. Based on range-compressed normalized backscatter and a modified wind retrieval algorithm, a coastal wind dataset at 12.5-km resolution was produced. Even with larger error, the high-resolution winds, in medium to high strength, would still be useful over coastal ocean. Using measurements from moored buoys from the National Buoy Data Center, the high-resolution QuikSCAT wind data are found to have similar accuracy as standard data in the open ocean. The accuracy of both high- and standard-resolution winds, particularly in wind directions, is found to degrade near shore. The increase in error is likely caused by the inadequacy of the geophysical model function/ambiguity removal scheme in addressing coastal conditions and light winds situations. The modified algorithm helps to bring the directional accuracy of the high-resolution winds to the accuracy of the standard-resolution winds in near-shore regions, particularly in the nadir and far zones across the satellite track.     

An analysis of SeaWinds-based rain retrieval in severe weather events

The Ku-band SeaWinds scatterometer estimates near-surface ocean wind vectors by relating measured backscatter to a geophysical model function for the near-surface vector wind. The conventional wind retrieval algorithm does not explicitly account for SeaWinds' sensitivity to rain, resulting in rain-caused wind retrieval error. A new retrieval method, termed "simultaneous wind/rain retrieval," that estimates both wind and rain from rain-contaminated measurements has been previously proposed and validated with Tropical Rain Measuring Mission data. Here, the accuracy of rains retrieved by the new method is validated through comparison with the Next Generation Weather Radar (NEXRAD) in coastal storm events. The rains detected by both sensors are comparable, though SeaWinds-estimated rains exhibit greater variability. The performance of simultaneous wind/rain retrieval in flagging excessively rain-contaminated winds is discussed and compared to existing methods. A new rain-only retrieval algorithm for use in rain-backscatter-dominated areas is proposed and tested. A simple noise model for SeaWinds rain estimates is developed, and Monte Carlo simulation is employed to verify the model. The model shows that SeaWinds rain estimates have a standard deviation of 2.5 mm/h, which is higher than the NEXRAD measurements. Thresholding SeaWinds rain estimates at 2 mm/h yields a better rain flag than current rain flag algorithms.     

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.     

On Bayesian scatterometer wind inversion

In a quest for a generic unbiased scatterometer wind inversion method, the different inversion procedures currently in use are revisited in this paper. A careful examination of both the errors in the wind and in the measurement domain, combined with the nonlinear shape of the geophysical model function (GMF), leads to a generic and novel Bayesian wind retrieval approach in the measurement domain. In this approach the shape of the GMF solution manifold in measurement space is more important than the specified noise. This shape is related to the system wind direction sensitivity, and when this sensitivity is uniform, realistic and precise wind direction distributions are retrieved, even when measurements lie far from the GMF manifold. A simplified measurement space transformation that produces such uniform sensitivity for the European Remote Sensing Satellite (ERS) scatterometer is presented and shown to have reduced wind direction bias compared to the more traditional (measurement-noise normalized) inversion for ERS. Moreover, the simplified wind inversion reveals a similar performance to the current operational ERS wind inversion, but is potentially more generally applicable. The simplified method is then applied to SeaWinds but is ineffective. In this case the instrument geometry results in a low sensitivity to wind direction at a few specific directions. As a consequence, certain wind direction solutions remain favored in the SeaWinds inversion.     

A Wind and Rain Backscatter Model Derived From AMSR and SeaWinds Data

The SeaWinds scatterometer was originally designed to measure wind vectors over the ocean by exploiting the relationship between wind-induced surface roughening and the normalized radar backscatter cross section. Rain can degrade scatterometer wind estimation; however, the simultaneous wind/rain (SWR) algorithm was developed to enable SeaWinds to simultaneously retrieve wind and rain rate data. This algorithm is based on colocating data from the Precipitation Radar on the Tropical Rainfall Measuring Mission and SeaWinds on QuikSCAT. This paper develops a new wind and rain radar backscatter model for SWR using colocated data from the Advanced Microwave Scanning Radiometer (AMSR) and SeaWinds aboard the Advanced Earth Observing Satellite II. This paper accounts for rain height in the model in order to calculate surface rain rate from the integrated rain rate. The performance of SWR using the new wind/rain model is measured by comparison of wind vectors and rain rates to the previous SWR algorithm, AMSR rain rates, and National Center for Environmental Prediction numerical weather prediction winds. The new SWR algorithm produces more accurate rain estimates and improved winds, and detects rain with a low false alarm rate.      

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