飓风登陆时总水位和沿岸变化预报的技能评估

IF 4.2 2区 工程技术 Q1 ENGINEERING, CIVIL
Justin J. Birchler , Margaret L. Palmsten , Kara S. Doran , Sharifa Karwandyar , Joshua M. Pardun , Elora M. Oades , Ryan P. Mulligan , Eli S. Whitehead-Zimmers
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引用次数: 0

摘要

总水位和海岸变化预报(TWL&CC 预报)为沿海社区提供为期 6 天的通知,预报沙 滩上可能出现的威胁安全、基础设施或资源的水位升高和海岸变化(即沙丘侵蚀、冲刷或淹没)。这一持续运行的模式每小时提供美国墨西哥湾和大西洋沿岸部分地区的信息。这项工作的目的是,在 2020 年 8 月伊萨亚斯飓风造成美国北卡罗来纳州(NC)和南卡罗来纳州沿岸水位升高期间,利用观测数据和模型后报相结合的方法,评估预测技能。在北卡罗来纳州赖茨维尔海滩附近的三个地点对整个风暴期间的水位和海浪进行了观测,为评估预报技能提供了信息:离岸波浪浮标、当地码头的验潮仪以及部署在码头的压力传感器。除观测数据外,在伊萨亚斯风暴最高峰期间,非静水相位解析模型 SWASH(Simulating WAves till SHore)还利用从 Delft3D-SWAN 耦合模拟中获得的每小时波浪能量谱进行强制计算,通过计算近岸波高和波浪引起的海岸线设置和上升,对观测数据进行补充。在风暴峰值期间,传感器位置的 SWASH 模拟水位与最大向陆范围的水位相当(偏差 = -0.05米;增益 = 0.26;r2 = 0.99),这表明 USGS 传感器位置的观测数据是 TWL&CC 预测的海岸线总水位(TWL;潮汐、浪涌和波浪上升的总和)的有用替代数据。Wrightsville 海滩的总水位(TWL)预测结果与 USGS 传感器的观测结果一致(两个预测模型网格的偏差分别为-0.38 米和-0.74 米,分散指数分别为 0.22 和 0.28;考虑到模型不确定性的加权回归解释了观测总水位(TWL)变化的 95%)。在暴雨峰值的 5 小时内,观测到的 TWL 均在 TWL&CC 预报的置信区间内。预报平均水位(MWL;潮汐、涌浪和波浪的总和)与验潮仪观测结果也一致(预报模式网格的偏差 = 0.07 米和 0.02 米;散布指数 = 0.46;r2 = 0.80)。在两个地点,风暴峰值时的预报最大最低水位与验潮仪观测到的最大最低水位相差不超过 0.06 米。在 "伊萨亚斯 "登陆地区,另外 8 个压力传感器与预测的峰值 TWL 进行了比较(偏差 = 0.14 米;散布指数 = 0.18)。当考虑到加权回归预测的不确定性时,预测的总温升解释了观测到的总温升变化的 90%。结果表明,在 "伊萨亚斯 "风暴期间,波浪驱动的水位在预测的双向浮标中占了很 大比例(在风暴的三个高峰时段占 52%),而且双向浮标在预测模式中得到了体现。在所考虑的两个预报模式网格中,沿岸变化预报和观测到的过冲量的平均绝对误差分别为 0.4 和 0.14。这种计算效率高的方法所显示的技术表明,预报系统可以在风暴威胁沿岸地区之前数天至数小时,以亚公里分辨率对数百公里海岸线上的 TWL 进行快速可靠的预报。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Skill assessment of a total water level and coastal change forecast during the landfall of a hurricane

The Total Water Level and Coastal Change Forecast (TWL&CC Forecast) provides coastal communities with 6-day notice of potential elevated water levels and coastal change (i.e., dune erosion, overwash, or inundation) on sandy beaches that threatens safety, infrastructure, or resources. This continuously operating model provides hourly information for select regions along U.S. Gulf of Mexico and Atlantic Ocean coastlines. The objective of this work is to assess the skill of forecasts during a period of elevated water levels along the coasts of North Carolina (NC) and South Carolina, USA caused by Hurricane Isaias in August 2020, using a combination of observations and model hindcasts. Water levels and waves were observed throughout the storm at three locations near Wrightsville Beach, NC, which provided information to assess forecast skill; a wave buoy offshore, a tide gage at a local pier, and a pressure sensor deployed at the pier. In addition to observations, the non-hydrostatic phase-resolving model SWASH (Simulating WAves till SHore) was forced with hourly wave energy spectra derived from a coupled Delft3D-SWAN simulation during the peak of Isaias, to complement observations by computing nearshore wave height and wave-induced setup and runup at the shoreline. During the storm peak, SWASH-simulated water levels at the sensor position were comparable to those at the maximum landward extent (bias = −0.05 m; gain = 0.26; r2 = 0.99), suggesting that observations at the USGS sensor location were a useful proxy for total water level (TWL; sum of tide, surge and wave runup) at the shoreline that are predicted by the TWL&CC Forecast. The TWL forecast at Wrightsville Beach was consistent with observations from the USGS sensor (bias = −0.38 m and −0.74 m, scatter index = 0.22 and 0.28 for the two forecast model grids considered, respectively; weighted regression considering model uncertainty explained 95 percent of variability in observed TWL). Observed TWL was within the confidence interval of the TWL&CC Forecast for the 5 h at the storm peak. Forecast mean water levels (MWL; sum of tide, surge and wave setup) and tide gage observations were also consistent (bias = 0.07 m and 0.02 m for the forecast model grids; scatter index = 0.46; r2 = 0.80). Forecast MWL at the storm peak was within 0.06 m of the observed MWL from the tide gage for both sites. In the region where Isaias made landfall, eight additional pressure sensors were compared to the peak TWL forecast (bias = 0.14 m; scatter index = 0.18). Forecast TWL explained 90 percent of observed variability in TWL when considering uncertainty of the forecast with a weighted regression. The results demonstrate that wave-driven water levels contributed a significant portion of the forecast TWL during Isaias (52 percent during the three peak hours of the storm), and that TWL were represented using the forecast model. Mean absolute error of the coastal change forecast and observed overwash is 0.4 and 0.14 for the two forecast model grids considered. The skill demonstrated by this computationally efficient method indicates that the forecasting system can provide fast and reliable predictions of TWL across hundreds of km of coastline at sub-km resolution, days to hours in advance of when storms threaten coastal regions.

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来源期刊
Coastal Engineering
Coastal Engineering 工程技术-工程:大洋
CiteScore
9.20
自引率
13.60%
发文量
0
审稿时长
3.5 months
期刊介绍: Coastal Engineering is an international medium for coastal engineers and scientists. Combining practical applications with modern technological and scientific approaches, such as mathematical and numerical modelling, laboratory and field observations and experiments, it publishes fundamental studies as well as case studies on the following aspects of coastal, harbour and offshore engineering: waves, currents and sediment transport; coastal, estuarine and offshore morphology; technical and functional design of coastal and harbour structures; morphological and environmental impact of coastal, harbour and offshore structures.
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