Volume 42 Issue 5
Oct.  2022
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LIU Shuai, LIN Wenming, LU Yunfei. Inherent Error and Temporal-Spatial Characteristics of GYGNSS Sea Surface Wind Speed (in Chinese). Chinese Journal of Space Science, 2022, 42(5): 1029-1037 doi: 10.11728/cjss2022.05.211101110
Citation: LIU Shuai, LIN Wenming, LU Yunfei. Inherent Error and Temporal-Spatial Characteristics of GYGNSS Sea Surface Wind Speed (in Chinese). Chinese Journal of Space Science, 2022, 42(5): 1029-1037 doi: 10.11728/cjss2022.05.211101110

Inherent Error and Temporal-Spatial Characteristics of GYGNSS Sea Surface Wind Speed

doi: 10.11728/cjss2022.05.211101110
  • Received Date: 2021-11-01
  • Accepted Date: 2021-12-30
  • Rev Recd Date: 2022-06-08
  • Available Online: 2022-09-16
  • Global Navigation Satellite System Reflectometry (GNSS-R) is a new technique for the remote sensing of sea surface wind speed. In terms of operational applications, it is necessary to perform a detailed and quantitative analysis on the GNSS-R wind speed. In this paper, wind data of the Cyclone Global Navigation Satellite System (CYGNSS) mission are used to evaluate the capability of GNSS-R in wind remote sensing. First, the collocated buoy winds, as well as the European Centre for Medium-Range Weather Forecasts (ECMWF) winds, are used to analyze the climatologic and the temporal-spatial characteristics of CYGNSS wind speeds. Second, a triple collocation analysis is used to estimate the inherent errors and the calibration coefficients of GYGNSS wind. It shows that the quality of CYGNSS wind is promising for wind speed below 10 m·s–1, but degrades remarkably at high wind conditions. Moreover, the wind speed error is consistent in the temporal domain, but shows certain dependency on the geographic location. Overall, the inherent error of CYGNSS wind speed is about 1.79 m·s–1. The results are not only relevant for the operational application of CYGNSS wind product, but also important for the further inter-calibration of CYGNSS signal.

     

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  • [1]
    RODRIGUEZ-ALVAREZ N, AKOS D M, ZAVOROTNY V U, et al. Airborne GNSS-R wind retrievals using delay–Doppler maps[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(1): 626-641 doi: 10.1109/TGRS.2012.2196437
    [2]
    UNWIN M, JALES P, TYE J, et al. Spaceborne GNSS-Reflectometry on TechDemoSat-1: early mission operations and exploitation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(10): 4525-4539 doi: 10.1109/JSTARS.2016.2603846
    [3]
    GRIECO G, STOFFELEN A, PORTABELLA M. Rationale of GNSS reflected delay–Doppler map (DDM) distortions induced by specular point inaccuracies[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 3-13 doi: 10.1109/JSTARS.2019.2938327
    [4]
    HUANG F X, GARRISON J L, LEIDNER S M, et al. A forward model for data assimilation of GNSS ocean Reflectometry delay-Doppler maps[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(3): 2643-2656 doi: 10.1109/TGRS.2020.3002801
    [5]
    LIN W M, PORTABELLA M, FOTI G, et al. Toward the generation of a wind geophysical model function for spaceborne GNSS-R[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(2): 655-666 doi: 10.1109/TGRS.2018.2859191
    [6]
    白伟华, 夏俊明, 万玮, 等. 中国GNSS-R机载实验综合评估: 河流遥感[J]. 科学通报, 2015, 60(17): 1527-1534 doi: 10.1007/s11434-015-0869-x

    BAI Weihua, XIA Junming, WAN Wei, et al. A first comprehensive evaluation of China's GNSS-R airborne campaign: partⅡ—river remote sensing[J]. Science Bulletin, 2015, 60(17): 1527-1534 doi: 10.1007/s11434-015-0869-x
    [7]
    金双根, 张勤耘, 钱晓东. 全球导航卫星系统反射测量(GNSS+R)最新进展与应用前景[J]. 测绘学报, 2017, 46(10): 1389-1398

    JIN Shuanggen, ZHANG Qinyun, QIAN Xiaodong. New progress and application prospects of global navigation satellite system Reflectometry (GNSS+R)[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10): 1389-1398
    [8]
    CLARIZIA M P, GOMMENGINGER C P, GLEASON S T, et al. Analysis of GNSS-R delay-Doppler maps from the UK-DMC satellite over the ocean[J]. Geophysical Research Letters, 2009, 36(2): L02608
    [9]
    CLARIZIA M P, RUF C S. Wind speed retrieval algorithm for the cyclone global navigation satellite system (CYGNSS) mission[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(8): 4419-4432 doi: 10.1109/TGRS.2016.2541343
    [10]
    杨东凯, 刘毅, 王峰. 星载GNSS-R海面风速反演方法研究[J]. 电子与信息学报, 2018, 40(2): 462-469

    YANG Dongkai, LIU Yi, WANG Feng. Ocean surface wind speed retrieval using spaceborne GNSS-R[J]. Journal of Electronics & Information Technology, 2018, 40(2): 462-469
    [11]
    RUF C S, GLEASON S, JELENAK Z, et al. The CYGNSS nanosatellite constellation hurricane mission[C]//2012 IEEE International Geoscience and Remote Sensing Symposium. Munich: IEEE, 2012: 214-216
    [12]
    Kim H , Lakshmi V , Kwon Y , et al. First attempt of global-scale assimilation of subdaily scale soil moisture estimates from CYGNSS and SMAP into a land surface model[J]. Environmental Research Letters, 2021, 16(7): 074041 (11 pp).
    [13]
    PASCUAL D, CLARIZIA M P, RUF C S. Spaceborne demonstration of GNSS-R scattering cross section sensitivity to wind direction[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 8006005
    [14]
    FOTI G, GOMMENGINGER C, JALES P, et al. Spaceborne GNSS reflectometry for ocean winds: first results from the UK TechDemoSat-1 mission[J]. Geophysical Research Letters, 2015, 42(13): 5435-5441 doi: 10.1002/2015GL064204
    [15]
    GRIECO G, STOFFELEN A, PORTABELLA M, et al. Quality control of delay-Doppler maps for stare processing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(5): 2990-3000 doi: 10.1109/TGRS.2018.2879059
    [16]
    CLARIZIA M P, RUF C S, JALES P, et al. Spaceborne GNSS-R minimum variance wind speed estimator[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(11): 6829-6843 doi: 10.1109/TGRS.2014.2303831
    [17]
    李伟强, 杨东凯, 李明里, 等. 面向遥感的GNSS反射信号接收处理系统及实验[J]. 武汉大学学报·信息科学版, 2011, 36(10): 1204-1208

    LI Weiqiang, YANG Dongkai, LI Mingli, et al. Design and experiments of GNSS-R receiver system for remote sensing[J]. Geomatics and Information Science of Wuhan University, 2011, 36(10): 1204-1208
    [18]
    袁国良, 张卫峰, 卫豪杰. 基于GNSS-R的反演海面风速技术的研究[J]. 微型机与应用, 2017, 36(13): 88-90,93

    YUAN Guoliang, ZHANG Weifeng, WEI Haojie. Sea surface wind speed measurement using GNSS reflection signal[J]. Microcomputer & Its Applications, 2017, 36(13): 88-90,93
    [19]
    骆黎明, 白伟华, 孙越强, 等. 基于树模型机器学习方法的GNSS-R海面风速反演[J]. 空间科学学报, 2020, 40(4): 595-601

    LUO Liming, BAI Weihua, SUN Yueqiang, et al. GNSS-R sea surface wind speed inversion based on tree model machine learning method[J]. Chinese Journal of Space Science, 2020, 40(4): 595-601
    [20]
    吕帆, 修春娣, 王峰, 等. GNSS-R海面风场反演模型仿真分析[J]. 导航定位学报, 2018, 6(3): 87-91,97

    LYU Fan, XIU Chundi, WANG Feng, et al. Simulation analysis on GNSS-R ocean surface wind field retrieval model[J]. Journal of Navigation and Positioning, 2018, 6(3): 87-91,97
    [21]
    LIU W T, KATSAROS K B, BUSINGER J A. Bulk parameterization of air-sea exchanges of heat and water vapor including the molecular constraints at the interface[J]. Journal of the Atmospheric Sciences, 1979, 36(9): 1722-1735 doi: 10.1175/1520-0469(1979)036<1722:BPOASE>2.0.CO;2
    [22]
    STOFFELEN A. Toward the true near-surface wind speed: error modeling and calibration using triple collocation[J]. Journal of Geophysical Research:Oceans, 1998, 103(C4): 7755-7766 doi: 10.1029/97JC03180
    [23]
    GRUBER A, DORIGO W A, CROW W, et al. Triple collocation-based merging of satellite soil moisture retrievals[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(12): 6780-6792 doi: 10.1109/TGRS.2017.2734070
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