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Uncertainty in Scatterometer-Derived Vorticity

机译：散射计衍生涡度的不确定性

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A more versatile and robust technique is developed for determining area-averaged surface vorticity based on vector winds from swaths of remotely sensed wind vectors. This technique could also be applied to determine the curl of stress, and it could be applied to any gridded dataset of winds or stresses. The technique is discussed in detail and compared to two previous studies that focused on early development of tropical systems. Error characteristics of the technique are examined in detail. Specifically, three independent sources of error are explored: random observational error, truncation error, and representation error. Observational errors are due to random errors in the wind observations and determined as a worst-case estimate as a function of averaging spatial scale. The observational uncertainty in the Quick Scatterometer (QuikSCAT)-derived vorticity averaged for a roughly circular shape with a 100-km diameter, expressed as one standard deviation, is approximately 0.5 × 10~(-5) s~(-1) for the methodology described herein. Truncation error is associated with the assumption of linear changes between wind vectors. Uncertainty related to truncation has more spatial organization in QuikSCAT data than observational uncertainty. On 25- and 50-km scales, the truncation errors are very large. The third type of error, representation error, is due to the size of the area being averaged compared to values with 25-km length scales. This type of error is analogous to oversmoothing. Tropical and subtropical low pressure systems from three months of QuikSCAT observations are used to examine truncation and representation errors. Representation error results in a bias of approximately -1.5 × 10~(-5) s~(-1) for area-averaged vorticity calculated on a 100-km scale compared to vorticity calculated on a 25-km scale. The discussion of these errors will benefit future projects of this nature as well as future satellite missions.
机译：开发了一种更加通用和鲁棒的技术，用于基于来自大量遥感风矢量的矢量风来确定面积平均的表面涡度。该技术也可以应用于确定应力的卷曲，并且可以应用于风或应力的任何网格化数据集。对该技术进行了详细的讨论，并与之前的两个研究重点放在热带系统的早期发展进行了比较。详细检查了该技术的错误特征。具体来说，探讨了三个独立的误差源：随机观察误差，截断误差和表示误差。观测误差是由于风观测中的随机误差引起的，并根据空间比例平均确定为最坏情况估计。快速散射仪（QuikSCAT）推导的涡旋度对于直径为100 km的大致圆形的平均形状的观测不确定度（表示为一个标准偏差）约为0.5×10〜（-5）s〜（-1）。本文描述的方法。截断误差与风矢量之间线性变化的假设有关。与观测不确定性相比，QuikSCAT数据中与截断相关的不确定性具有更多的空间组织。在25公里和50公里的规模上，截断误差非常大。第三种误差，表示误差，是由于与25公里长度刻度的值相比，该区域的大小被平均了。这种类型的错误类似于过度平滑。来自三个月的QuikSCAT观测结果的热带和亚热带低压系统用于检查截断和表示误差。与在25 km尺度上计算出的涡度相比，对于100 km尺度上计算出的面积平均涡度，表示误差导致的偏差约为-1.5×10〜（-5）s〜（-1）。对这些错误的讨论将使这种性质的未来项目以及未来的卫星任务受益。

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  • 来源
    《Journal of atmospheric and oceanic technology》 |2010年第3期|p.594-603|共10页
  • 作者

    Mark A. Bourassa; Kelly McBeth Ford;

  • 作者单位

    Department of Meteorology, and Center for Ocean-Atmospheric Prediction Studies, The Florida State University, Tallahassee, Florida;

    Department of Meteorology, and Center for Ocean-Atmospheric Prediction Studies, The Florida State University, Tallahassee, Florida;

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