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Sequential Processing of GNSS-R Delay-Doppler Maps to Estimate the Ocean Surface Wind Field

机译:GNSS-R延迟多普勒图的顺序处理以估计海面风场

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Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) measurements of ocean winds present a challenge because the surface wind speed cannot be assumed homogeneous over the large glistening zone observable from orbit. The 25-km resolution requirement of the Cyclone GNSS (CYGNSS) mission limits wind speed retrievals to using only a small fraction of the delay-Doppler map (DDM) near the specular point. This paper presents a new method to invert a larger portion of the DDM and estimate the wind field within a swath defined by the maximum delay. An extended Kalman filter (EKF) is applied to combine a series of DDMs and estimates the wind speed on a uniformly gridded ocean surface exploiting the large overlap between sequential looks. Wind retrievals at the specular point using simulated data generated using wind fields from two hurricanes (Danielle and Earl, 2010) were found to meet the CYGNSS measurement requirements (2 m/s for U 20 m/s and 10 for U 20 m/s) and perform better than a typical single-point observable. Wind retrievals were also found to meet these requirements below 20 m/s within a 90-km swath and meet them for hurricane-force winds (30 m/s) within a 22-km swath. Similar results were obtained using a compressed ($17 imes 11$ ) DDM, demonstrating benefits of sequential processing within a limited data budget. Delay-Doppler ambiguities introduced noticeable artifacts in circumstances where the wind varies asymmetrically within the observed ($90imes 90$ km) surface area but did not appear to affect specular point wind retrievals.
机译:星空全球导航卫星系统反射法(GNSS-R)对海风的测量提出了挑战,因为在从轨道上可观察到的大闪闪区域上,不能假定表面风速是均匀的。气旋GNSS(CYGNSS)任务需要25公里的分辨率,这限制了风速的获取只能使用镜面反射点附近的一小部分延迟多普勒图(DDM)。本文提出了一种新方法,可以对DDM的较大部分进行反演,并估计由最大延迟定义的范围内的风场。应用扩展卡尔曼滤波器(EKF)来组合一系列DDM,并利用相继外观之间的较大重叠来估计均匀网格化的海洋表面上的风速。发现使用来自两个飓风的风场生成的模拟数据在镜面反射点进行风取回(Danielle和Earl,2010年)符合CYGNSS测量要求(U 20 m / s为2 m / s,U 20 m / s为10 m / s ),并且比典型的单点可观察性更好。还发现在90 km范围内风回收满足20 m / s以下的这些要求,在22 km范围内风飓风(30 m / s)满足这些要求。使用压缩的($ 17 x11 $)DDM获得了相似的结果,证明了在有限的数据预算内进行顺序处理的好处。延迟多普勒模糊性引入了明显的假象,在这种情况下,风在观察到的($ 90 x 90 $ km)表面积内非对称变化,但似乎不影响镜面点风的取回。

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