模拟沿海复杂形状场地风力的挑战:马德拉岛

I. L. Coimbra, J. M. L. M. Palma
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摘要

马德拉岛的风向受错综复杂的海岸线和多山地形的影响,这些特征在数值模型中不易体现。因此,本研究针对马德拉岛的沿海和复杂地形,利用 WRF(1 千米网格)和 VENTOS®/M(高分辨率区域 50 米网格)模型建立了中尺度到微尺度模型链,并提出了一些挑战和可能的解决方案。四座气象塔(分别位于半岛东部-T0978/E 以及东南部-T0521/SE、南部-T0522/S 和北部-T0960/N 海岸)的风力测量数据可作为评估模拟结果的参考。首先,由于 WRF 的地表位置,陆地区域被误划为海洋(反之亦然)。为了解决这个问题,我们改变了域的位置,以便更好地分配东半岛的陆块,从而改进了 T0978/E 处的近地表风模拟(均方根误差和偏差分别减少了 19% 和 67%)。其次,WRF 默认的海温变量插值没有考虑缺失和屏蔽数据。因此,采用了不同的 SST 插值方法,从而改进了 T0960/N 和 T0522/S桅杆的近地表风模拟(均方根误差和偏差分别减少了 11%和 84%),但 T0978/E桅 杆的误差较大(分别增加了 7%和 45%)。这种负面影响是由于新的内插法的加速不正确造成的。第三,在 WRF 中评估了 SST_SKIN 的影响,SST_SKIN 会影响表层的温度分布。启动 SST_SKIN 后,仅 T0521/SE 塔的近地面风模拟略有改善(均方根误差和偏差分别减少了 2% 和 6%),这可能是由于该区域的大气环流以较小尺度为主,与其他塔的环流以信风(N 和 E 塔)和海岛尾流(S 塔)为主形成鲜明对比。当使用 WRF 输出作为边界条件时,微尺度运行受到的这些影响没有中尺度结果那么明显。尽管如此,VENTOS®/M近地面风模拟的均方根误差和偏差在T0978/E处分别降低了6%和9%。
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Challenges in simulating the wind over a coastal complex-shaped site: Madeira Island
The wind behaviour on Madeira Island is shaped by the intricate coastline and mountainous terrain, features that are not easily represented in numerical models. Thus, this study addressed some challenges and possible solutions in setting up a meso-to-microscale model chain, with the WRF (1-km mesh) and VENTOS®/M (50-m mesh in the high-resolution area) models, in Madeira’s coastal and complex terrain. Wind measurements from four meteorological towers (located in the eastern peninsula–T0978/E–and on the SE–T0521/SE, S–T0522/S, and N–T0960/N–coasts) served as references to assess the simulations. First, due to WRF’s landmask position, land areas were misclassified as sea (and vice versa). This issue was addressed by altering the domain position to better allocate the landmask on the east peninsula, resulting in improved near-surface wind simulations at T0978/E (reducing RMSE and bias by 19% and 67%). Secondly, WRF’s default interpolation of the SST variable did not account for missing and masked data. As such, a different SST interpolation method was employed, leading to improved near-surface wind simulations at T0960/N (reducing RMSE and bias by 11% and 84%) and T0522/S (10% and 16% reduction) masts, but higher errors at T0978/E (7% and 45% increase). The negative influence arose from an incorrect speedup with the new interpolation method. Thirdly, the impact of SST_SKIN, which influences the temperature distribution at the skin level, was evaluated in WRF. Activating SST_SKIN led to a slight improvement in the near-surface wind simulation only at T0521/SE (2% and 6% RMSE and bias reduction), probably due to the dominant smaller-scale nature of the atmospheric circulation in the area, which contrasts with the circulation at the other towers, dominated by the trade winds (N and E masts) and the Island’s wake (S mast). When using the WRF outputs as boundary conditions, these effects on the microscale runs were less pronounced than on the mesoscale results. Nonetheless, the RMSE and bias of the near-surface wind simulation in VENTOS®/M were reduced by 6% and 9% at T0978/E.
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