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Retrieval of sea surface winds under hurricane conditions from GNSS-R observations

机译:从GNSS-R观测中检索飓风条件下的海面风

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摘要

Reflected signals from global navigation satellite systems (GNSSs) have been widely acknowledged as an important remote sensing tool for retrieving sea surface wind speeds. The power of GNSS reflectometry (GNSS-R) signals can be mapped in delay chips and Doppler frequency space to generate delay Doppler power maps (DDMs), whose characteristics are related to sea surface roughness and can be used to retrieve wind speeds. However, the bistatic radar cross section (BRCS), which is strongly related to the sea surface roughness, is extensively used in radar. Therefore, a bistatic radar cross section (BRCS) map with a modified BRCS equation in a GNSS-R application is introduced. On the BRCS map, three observables are proposed to represent the sea surface roughness to establish a relationship with the sea surface wind speed. Airborne Hurricane Dennis (2005) GNSS-R data are then used. More than 16 000 BRCS maps are generated to establish GMFs of the three observables. Finally, the proposed model and classic one-dimensional delay waveform (DW) matching methods are compared, and the proposed model demonstrates a better performance for the high wind speed retrievals.
机译:来自全球导航卫星系统(GNSS)的反射信号已被公认为是获取海面风速的重要遥感工具。 GNSS反射法(GNSS-R)信号的功率可以映射到延迟码片和多普勒频率空间中,以生成延迟多普勒功率图(DDM),其特性与海面粗糙度有关,可以用来获取风速。但是,与海面粗糙度密切相关的双基地雷达横截面(BRCS)已广泛用于雷达中。因此,在GNSS-R应用程序中,引入了带有修改后的BRCS方程的双基地雷达横截面(BRCS)图。在BRCS地图上,提出了三个可观测值来表示海面粗糙度,以建立与海面风速的关系。然后使用机载飓风丹尼斯(2005)GNSS-R数据。生成了超过16000个BRCS映射,以建立三个可观测值的GMF。最后,将提出的模型与经典的一维延迟波形(DW)匹配方法进行了比较,并证明了该模型在高风速检索中具有更好的性能。

著录项

  • 来源
    《海洋学报(英文版)》 |2016年第9期|91-97|共7页
  • 作者单位

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;

    National Engineering Center for Geoinformatics, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;

  • 收录信息 中国科学引文数据库(CSCD);中国科技论文与引文数据库(CSTPCD);
  • 原文格式 PDF
  • 正文语种 eng
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