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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing. >Deriving L-Band Tilting Ocean Surface Roughness From Measurements by Operational Systems
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Deriving L-Band Tilting Ocean Surface Roughness From Measurements by Operational Systems

机译：通过操作系统的测量导出L频带倾斜海面粗糙度

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摘要

Waves much shorter than those measured by operational systems make significant contribution to the ocean surface roughness. This article describes a method to obtain the L-band tilting ocean surface roughness using wind speed and windsea dominant wave period coupled with a wind–wave spectrum model. Examples are presented with wind and dominant wave data from ocean buoys and hurricane hunters. Several related issues are discussed: high-frequency wave spectrum, integration limit, swell contribution, and measurements in extreme winds: 1) it is well known since the 1970s that with stationary sensors, extending the frequency range in measuring elevation spectrum does not yield useful short-wave information because of the low signal level and large Doppler frequency shift involved in measuring short waves. 2) Low-pass mean square slopes (LPMSSs) integrated to 5 and 11 rad/m are computed to quantify their difference as a function of wind speed and inverse wave age (IWA). The normalized difference decreases with increasing wind speed and decreasing IWA. 3) Swell contribution to the L-band LPMSS is almost negligible for wind speed greater than 5 m/s (less than 5% in 99% of observations). In low-wind conditions (wind speed less than 5 m/s), the swell contribution is difficult to assess because of inaccuracy in identifying the weak windsea system. 4) The coarse resolution in National Data Buoy Center (NDBC) wave spectra causes large data scatter in the computed LPMSS in very high winds (greater than 20 m/s). A mitigating solution is offered.
机译：通过操作系统测量的波浪短得多，对海洋表面粗糙度产生了重大贡献。本文介绍了一种利用风波谱模型耦合的风速和帆板部分主导波周获得L波段倾斜海面粗糙度的方法。示例具有来自海洋浮标和飓风猎人的风和主波数据。讨论了几个相关问题：极大风的高频波谱，集成限制，膨胀贡献和测量：1）自20世纪70年代以来，众所周知，通过静止传感器，延伸测量升高频谱中的频率范围不会产生短波信息由于信号电平低，并且涉及测量短波中的大多普勒频率。 2）计算到5和11 rad / m集成到5和11 rad / m的低通均方斜率（LPMSS）以量化它们作为风速和逆波年龄（IWA）的函数的差异。归一化差异随着风速的增加和减少IWA而降低。 3）对于L频率的膨胀贡献几乎可以忽略于大于5米/秒的风速（少于99％的观察结果）。在低风力条件下（风速小于5米/秒），由于识别弱帆板系统的不准确性，膨胀贡献难以评估。 4）国家数据浮标中心（NDBC）波谱中的粗略分辨率在非常高的风中导致计算的LPMS中的大数据散射（大于20米/秒）。提供了缓解解决方案。

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