Following Ekman’s 1905 mathematical description of the influence of wind stress on the ocean, measurement of the wind field itself has been a critical part of any effort to understand the movement of ocean currents. This is particularly true in the coastal zone, where winds and waves interact with the coastal boundary to drive spatially and temporally complex currents. In addition to understanding and predicting coastal flows, observations of near shore surface winds are fundamental to fulfilling both scientific (e.g. circulation, mixing, biological productivity, larval transport) and societal needs (e.g. shipping, wind power). Near shore wind observations, either in situ or remote, typically have high temporal resolution, or high spatial resolution, but not both. Buoy-based observations, such as those accessible via the National Data Buoy Center (NDBC), provide time series of wind products from most coastal areas but lack the spatially relevant resolution for observing many – if not most – of the critical small scale circulation processes. Recent developments have improved satellite scatterometer capabilities to within 10-15 km from shore (e.g.[2, 3]), and similarly, Synthetic Aperture Radar (SAR) achieves sub-km resolution (0.5-1 km) up to 1-3 km from shore with RMS errors of 1.4-1.8 m s−1 [4, 5]. While planned future missions such as the Waves and Currents Mission (WACM) [6] would further advance these techniques and provide maps of winds with high spatial resolution, satellite-based observations sample at periods of 12 hours or greater, limiting their scientific utility. In contrast, land-based HF radar systems routinely provide both high spatial and high temporal resolution observations of surface currents in the coastal ocean in all weather conditions. The fundamental signal observed by the radar system results from the presence of relatively short ocean waves that respond quickly to changes in wind speed and direction. In the near shore, wind observations from these systems would fill an important niche between satellite observations, which encounter difficulties close to land masses, and moored observations, for which spatially dense deployments are cost prohibitive. The spatial and temporal coverage possible, with time scales of 10s of minutes and spatial scales of 2-6 kilometers, matches the most likely resolution needed to advance the present understand and modelling of coastal ocean circulation [7, 8, 9].
继埃克曼1905年对风应力对海洋影响的数学描述之后，对风场本身的测量已经成为了解洋流运动的关键部分。在沿海地区尤其如此，那里的风和波与海岸边界相互作用，驱动空间和时间上复杂的洋流。除了了解和预测沿海气流外，对近岸表面风的观测对于满足科学需求(如环流、混合、生物生产力、幼虫运输)和社会需求(如航运、风力发电)也是至关重要的。近岸风观测，无论是原位还是远程，通常具有高时间分辨率，或高空间分辨率，但不是两者兼而有之。基于浮标的观测，例如通过国家数据浮标中心(NDBC)获得的观测，提供了来自大多数沿海地区的风产品的时间序列，但缺乏观测许多(如果不是大多数的话)关键小尺度环流过程的空间相关分辨率。最近的发展已经提高了卫星散射计的能力，使其距离海岸10-15公里(例如[2,3])，同样，合成孔径雷达(SAR)在距离海岸1-3公里的范围内实现了亚公里分辨率(0.5-1公里)，RMS误差为1.4-1.8 m s−1[4,5]。虽然计划中的未来任务，如波浪和洋流任务(WACM)[6]将进一步推进这些技术，并提供高空间分辨率的风图，但基于卫星的观测样本周期为12小时或更长，限制了它们的科学效用。相比之下，陆基高频雷达系统通常在所有天气条件下提供高空间和高时间分辨率的沿海海洋表面流观测。雷达系统观测到的基本信号来自相对较短的海浪，这些海浪对风速和风向的变化反应迅速。在近岸，来自这些系统的风观测将填补卫星观测和停泊观测之间的重要空白，卫星观测在靠近陆地时遇到困难，而停泊观测在空间上密集部署成本过高。时间尺度为10分钟，空间尺度为2-6公里时，可能的时空覆盖范围与推进目前对沿海海洋环流的理解和模拟所需的最可能分辨率相匹配[7,8,9]。
{"title":"HF radar observation of nearshore winds","authors":"B. Emery, A. Kirincich","doi":"10.1049/sbra537e_ch8","DOIUrl":"https://doi.org/10.1049/sbra537e_ch8","url":null,"abstract":"Following Ekman’s 1905 mathematical description of the influence of wind stress on the ocean, measurement of the wind field itself has been a critical part of any effort to understand the movement of ocean currents. This is particularly true in the coastal zone, where winds and waves interact with the coastal boundary to drive spatially and temporally complex currents. In addition to understanding and predicting coastal flows, observations of near shore surface winds are fundamental to fulfilling both scientific (e.g. circulation, mixing, biological productivity, larval transport) and societal needs (e.g. shipping, wind power). Near shore wind observations, either in situ or remote, typically have high temporal resolution, or high spatial resolution, but not both. Buoy-based observations, such as those accessible via the National Data Buoy Center (NDBC), provide time series of wind products from most coastal areas but lack the spatially relevant resolution for observing many – if not most – of the critical small scale circulation processes. Recent developments have improved satellite scatterometer capabilities to within 10-15 km from shore (e.g.[2, 3]), and similarly, Synthetic Aperture Radar (SAR) achieves sub-km resolution (0.5-1 km) up to 1-3 km from shore with RMS errors of 1.4-1.8 m s−1 [4, 5]. While planned future missions such as the Waves and Currents Mission (WACM) [6] would further advance these techniques and provide maps of winds with high spatial resolution, satellite-based observations sample at periods of 12 hours or greater, limiting their scientific utility. In contrast, land-based HF radar systems routinely provide both high spatial and high temporal resolution observations of surface currents in the coastal ocean in all weather conditions. The fundamental signal observed by the radar system results from the presence of relatively short ocean waves that respond quickly to changes in wind speed and direction. In the near shore, wind observations from these systems would fill an important niche between satellite observations, which encounter difficulties close to land masses, and moored observations, for which spatially dense deployments are cost prohibitive. The spatial and temporal coverage possible, with time scales of 10s of minutes and spatial scales of 2-6 kilometers, matches the most likely resolution needed to advance the present understand and modelling of coastal ocean circulation [7, 8, 9].","PeriodicalId":117997,"journal":{"name":"Ocean Remote Sensing Technologies: High frequency, marine and GNSS-based radar","volume":"205 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131908093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Sea surface current mapping with HF radar – a primer","authors":"C. Merz, Yonggang Liu, R. Weisberg","doi":"10.1049/sbra537e_ch4","DOIUrl":"https://doi.org/10.1049/sbra537e_ch4","url":null,"abstract":"","PeriodicalId":117997,"journal":{"name":"Ocean Remote Sensing Technologies: High frequency, marine and GNSS-based radar","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133513969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Wavelet-based methods to invert sea surfaces and bathymetries from X-band radar images","authors":"P. Chernyshov, T. Vrećica, Y. Toledo","doi":"10.1049/sbra537e_ch13","DOIUrl":"https://doi.org/10.1049/sbra537e_ch13","url":null,"abstract":"","PeriodicalId":117997,"journal":{"name":"Ocean Remote Sensing Technologies: High frequency, marine and GNSS-based radar","volume":"247 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121484130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"HF radar in a maritime environment","authors":"E. Gill, Weimin Huang","doi":"10.1049/sbra537e_ch1","DOIUrl":"https://doi.org/10.1049/sbra537e_ch1","url":null,"abstract":"","PeriodicalId":117997,"journal":{"name":"Ocean Remote Sensing Technologies: High frequency, marine and GNSS-based radar","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134471586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this chapter we review methods by which near--surface ocean currents can be measured remotely using images of the water surface, as obtained by X-band radar in particular. The presence of a current changes the dispersive behavior of surface waves, so our challenge is to solve the inverse problem: to infer the spatially-varying current from measurements of the wavy surface. We examine how remote sensing of currents is achieved in practice by analyzing the wave spectrum, as may be measured for example by X-band radar. A set of consecutive backscatter images recorded as a function of time is Fourier-transformed to produce the spectrum, which gives information concerning the propagation of waves whose dispersion is altered by currents. X-band radar images measure the wave field over multiple square kilometers, and analyzing various spatial subsets of the images allows a map of the spatial variation of the currents to be reconstructed. Several algorithms for obtaining empirical dispersion relations from the measured spectrum and extracting the currents are reviewed: the least squares and iterative least squares method, the normalized scalar product method, and the polar current shell method. We go on to describe how the same methods and algorithms can be extended to also allowing the depth-dependence of the current to be determined. Reasonable agreement between radar-derived currents and in situ measurements has been demonstrated in multiple field measurements. However, more validation is necessary especially in the context of depth-varying flows. Understanding the extent to which Stokes drift is measured as part of the radar-derived current is not well-understood yet potentially important.
{"title":"Current mapping from the wave spectrum","authors":"B. Smeltzer, S. Ellingsen","doi":"10.1049/SBRA537E_ch15","DOIUrl":"https://doi.org/10.1049/SBRA537E_ch15","url":null,"abstract":"In this chapter we review methods by which near--surface ocean currents can be measured remotely using images of the water surface, as obtained by X-band radar in particular. The presence of a current changes the dispersive behavior of surface waves, so our challenge is to solve the inverse problem: to infer the spatially-varying current from measurements of the wavy surface. We examine how remote sensing of currents is achieved in practice by analyzing the wave spectrum, as may be measured for example by X-band radar. A set of consecutive backscatter images recorded as a function of time is Fourier-transformed to produce the spectrum, which gives information concerning the propagation of waves whose dispersion is altered by currents. X-band radar images measure the wave field over multiple square kilometers, and analyzing various spatial subsets of the images allows a map of the spatial variation of the currents to be reconstructed. Several algorithms for obtaining empirical dispersion relations from the measured spectrum and extracting the currents are reviewed: the least squares and iterative least squares method, the normalized scalar product method, and the polar current shell method. We go on to describe how the same methods and algorithms can be extended to also allowing the depth-dependence of the current to be determined. Reasonable agreement between radar-derived currents and in situ measurements has been demonstrated in multiple field measurements. However, more validation is necessary especially in the context of depth-varying flows. Understanding the extent to which Stokes drift is measured as part of the radar-derived current is not well-understood yet potentially important.","PeriodicalId":117997,"journal":{"name":"Ocean Remote Sensing Technologies: High frequency, marine and GNSS-based radar","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132807878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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