Inherent Error and Temporal-Spatial Characteristics of GYGNSS Sea Surface Wind Speed
-
摘要: 全球导航卫星系统反射测量(GNSS-R)是一种新兴的海面风速遥感技术,对GNSS-R反演风速进行详细定量分析是该技术从科学研究走向业务应用的必要条件。 以气旋全球导航卫星系统(CYGNSS)的风速数据为例,利用时空匹配的浮标风速和欧洲中期天气预报中心(ECMWF)的预报风速数据,详细分析了CYGNSS遥感风速的气候态特征和时空分布特征。基于三配对数据分析方法,阐明了CYGNSS遥感风速的固有误差,并提出了相应的风速标定系数。研究表明:GYGNSS的中低风速(w <10 m·s–1)精度较好,但高风速的误差显著增大;风速误差具有良好的时间一致性,但呈现明显的空间分布不均匀现象;总体而言,CYGNSS风速的固有误差约为1.79 m·s–1。研究结果一方面可为CYGNSS风速数据的业务应用提供参考,另一方面也为进一步标定CYGNSS的反射测量信号提供依据。
-
关键词:
- 全球导航卫星系统反射计 /
- 海面风速 /
- 固有误差 /
- 时空分布 /
- 三配对分析
Abstract: 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. -
图 3 CYGNSS(a)和ECMWF(b)年平均风速的空间分布,其风速变异性(不同网格点范围内风速的标准差)的空间分布(c)(d)(网格的大小为0.125°×0.125°)
Figure 3. Geographic distribution of the annual mean wind speed for CYGNSS (a) and ECMWF (b). The spatial distribution of wind variability for CYGNSS (c) and ECMWF (d) (Standard Deviation of wind speed in different grid points) (Grid size is 0.125°×0.125°)
图 4 (a)所有卫星CYGNSS和ECMWF风速差异的均值,(b)不同卫星风速偏差随月份的变化,(c)CYGNSS和ECMWF风速差异的标准差,(d)不同卫星风速的标准差随月份的变化。(参考风速为匹配的ECMWF预报风速)
Figure 4. Monthly distribution of CYGNSS wind speed bias for all satellites data (a) and each individual satellite (b). (c) (d) are as same as (a) and (b), but for the standard deviation of the wind speed difference between CYGNSS and ECMWF
表 1 浮标、CYGNSS和ECMWF风速的校正系数与固有误差
Table 1. Correction factors and inherent errors of buoy, CYGNSS and ECMWF wind speeds
单位 比例因子 偏差系数 固有误差/
(m·s–1)浮标 1.00 0.00 1.06 CYGNSS 1.28 –0.51 1.79 ECMWF 1.02 0.04 1.00 表 2 第一组数据浮标、CYGNSS和ECMWF风速的校正系数与固有误差
Table 2. Correction factors and inherent errors for the first group data set
单位 比例因子 偏差系数 固有误差/
(m·s–1)浮标 1.00 0.00 1.06 CYGNSS 1.18 –0.46 1.78 ECMWF 1.02 0.04 0.99 表 3 第二组数据浮标、CYGNSS和ECMWF风速的校正系数与固有误差
Table 3. Correction factors and inherent errors for the second group data set
单位 比例因子 偏差系数 固有误差/
(m·s–1)浮标 1 0 1.06 CYGNSS 1.35 –0.51 1.80 ECMWF 1.02 0.04 1.01 -
[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-xBAI 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): 1389-1398JIN 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): 462-469YANG 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): 1204-1208LI 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): 88-90,93YUAN 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): 595-601LUO 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): 87-91,97LYU 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