Study of global ocean waves based on SAR image cross-spectrum
-
摘要: 星载合成孔径雷达(Synthetic Aperture Radar, SAR)因其全天时全天候的观测能力,为全球海洋动力环境要素研究提供了重要数据支撑。然而,SAR海浪成像是非线性过程,现有理论中的近似求解会导致海浪谱反演的信息缺失。SAR图像交叉谱技术的提出一定程度上突破了这一限制,能够很好地量化海浪谱特性及海浪传播方向。本研究延续前人系列成果,利用最新提出的面向径向海浪的图像谱强度,开展不同尺度海浪随局地风速的变化趋势分析,并基于雷达视向图像谱强度提取了谱峰波数,进而结合欧洲空间局环境遥感卫星波模式在开阔大洋获取的近400万景SAR图像,分析了谱峰波数的全球分布特征分析,为量化全球风浪耦合过程提供新视角,揭示了海浪与风速耦合关系的空间分布与季节变化规律。
-
关键词:
- 星载合成孔径雷达 /
- 欧洲空间局环境遥感卫星 /
- 图像交叉谱 /
- 风浪耦合
Abstract: Spaceborne synthetic aperture radar (SAR) is able to collect observations under all kinds of weather during day and night. Such measurements have been proven to provide significant data support for the ocean dynamics study. While SAR imaging of ocean waves is a highly nonlinear process, leading the wave signal missing along the azimuth direction. The image cross-spectrum provides a way to help investigate the ocean wave features particularly for their propagation direction. In this study, we extended a recently defined parameter based on SAR image cross-spectrum and analyzed the correlation of different wave scales with the local wind speed. The range peak wavenumber (wavelength) extracted from the range spectral profile is also demonstrated at the global scale based on about 4 million SAR images. It is found that this new spectral parameter could to some extent reflect the coupling between wind and waves. The global pattern of range peak wavenumber also illustrates evident seasonality. -
图 6 全球径向峰值波数的季节变化
a-d分别为春、夏、秋、冬;等值线为0.04 rad/m;空间分辨率为2.5° × 2.5°
Fig. 6 Seasonal average of range peak wavenumber at the global scale
a-d are winter, spring, summer and autumn; the contour line denotes the wavenumber equal to 0.04 rad/m; the spatial bin is 2.5° × 2.5° for both latitude and longitude
-
[1] Toba Y. Local balance in the air-sea boundary processes: I. On the growth process of wind waves[J]. Journal of Oceanography, 1972, 28(3): 109−120. doi: 10.1007/BF02109772 [2] 陈戈, 杨杰, 张本涛, 等. 新一代海洋科学卫星的思考与展望[J]. 中国海洋大学学报, 2019, 49(10): 110−117.Chen Ge, Yang Jie, Zhang Bentao, et al. Thoughts and prospects on the new generation of marine science satellites[J]. Periodical of Ocean University of China, 2019, 49(10): 110−117. [3] Hauser D, Tison C, Amiot T, et al. SWIM: the first spaceborne wave scatterometer[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(5): 3000−3014. doi: 10.1109/TGRS.2017.2658672 [4] Young I R. Seasonal variability of the global ocean wind and wave climate[J]. International Journal of Climatology, 1999, 19(9): 931−950. doi: 10.1002/(SICI)1097-0088(199907)19:9<931::AID-JOC412>3.0.CO;2-O [5] Young I R, Zieger S, Babanin A V. Global trends in wind speed and wave height[J]. Science, 2011, 332(6028): 451−455. doi: 10.1126/science.1197219 [6] Hanley K E, Belcher S E, Sullivan P P. A global climatology of wind-wave interaction[J]. Journal of Physical Oceanography, 2010, 40(6): 1263−1282. doi: 10.1175/2010JPO4377.1 [7] Stopa J E, Cheung K F, Tolman H L, et al. Patterns and cycles in the climate forecast system reanalysis wind and wave data[J]. Ocean Modelling, 2013, 70: 207−220. doi: 10.1016/j.ocemod.2012.10.005 [8] Chen Ge, Chapron B, Ezraty R, et al. A global view of swell and wind sea climate in the ocean by satellite altimeter and scatterometer[J]. Journal of Atmospheric and Oceanic Technology, 2002, 19(11): 1849−1859. doi: 10.1175/1520-0426(2002)019<1849:AGVOSA>2.0.CO;2 [9] Jiang Haoyu, Chen Ge. A global view on the swell and wind sea climate by the jason-1 mission: a revisit[J]. Journal of Atmospheric and Oceanic Technology, 2013, 30(8): 1833−1841. doi: 10.1175/JTECH-D-12-00180.1 [10] Shimura T, Mori N, Mase H. Future projections of extreme ocean wave climates and the relation to tropical cyclones: ensemble experiments of MRI-AGCM3.2H[J]. Journal of Climate, 2015, 28(24): 9838−9856. doi: 10.1175/JCLI-D-14-00711.1 [11] Portilla-Yandún J. The global signature of ocean wave spectra[J]. Geophysical Research Letters, 2018, 45(1): 267−276. doi: 10.1002/2017GL076431 [12] 杨劲松. 合成孔径雷达海面风场、海浪和内波遥感技术[D]. 青岛: 中国海洋大学, 2001.Yang Jingsong. SAR remote sensing of sea surface wind field, ocean waves and internal waves [D]. Qingdao: Ocean University of China, 2001. [13] Stopa J E, Ardhuin F, Husson R, et al. Swell dissipation from 10 years of Envisat advanced synthetic aperture radar in wave mode[J]. Geophysical Research Letters, 2016, 43(7): 3423−3430. doi: 10.1002/2015GL067566 [14] Ardhuin F, Collard F, Chapron B, et al. Estimates of ocean wave heights and attenuation in sea ice using the SAR wave mode on Sentinel-1A[J]. Geophysical Research Letters, 2015, 42(7): 2317−2325. doi: 10.1002/2014GL062940 [15] Li Xiaoming. A new insight from space into swell propagation and crossing in the global oceans[J]. Geophysical Research Letters, 2016, 43(10): 5202−5209. doi: 10.1002/2016GL068702 [16] Li Huimin, Chapron B, Mouche A, et al. A new ocean SAR cross‐spectral parameter: definition and directional property using the global sentinel‐1 measurements[J]. Journal of Geophysical Research:Oceans, 2019, 124(3): 1566−1577. doi: 10.1029/2018JC014638 [17] Li Huimin, Stopa J, Mouche A, et al. Assessment of ocean wave spectrum using global Envisat/ASAR data and hindcast simulation[J]. Remote Sensing of Environment, 2021, 264: 112614. doi: 10.1016/j.rse.2021.112614