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Investigating predictability of offshore winds using a mesoscale model driven by forecast and reanalysis data
Meteorologische Zeitschrift ( IF 1.2 ) Pub Date : 2020-08-04 , DOI: 10.1127/metz/2019/1002
Sven-Erik Gryning , Rogier Floors

The atmosphere is inherently unpredictable by deterministic Numerical Weather Prediction models at both small and large temporal and spatial scales with some intermediate regime where predictability has been demonstrated; this study deals with time scales only. The chaotic nature at the smaller time scales is predominantly caused by turbulence and at the large scales by non-linearity of the Navier-Stokes equations. We investigate, based on observations carried out with a wind-lidar at the FINO3 research platform in the North Sea, the ability of the Weather Research and Forecasting model (WRF) to simulate the changes in the observations ahead of time. The simulations are performed in two ways. In one type the model uses boundary conditions from a reanalysis data-set (WRF-ERA). Alternatively, the simulations are carried out using boundary conditions from a forecast (WRF-GFS). In this study focus is on the predictability of changes in the wind speed and direction. A metric is suggested that chiefly accounts for point-wise changes in the wind speed and direction including turbulent structures. However, for completeness, a traditional metric that compared predicted and observed wind speed and direction directly is also applied. This metric does not reflect the turbulent structures of the flow for small lead times, as the new metric does. The traditional metric reveals very good skills (Fig. 2) up to a lead time of 4 days for simulations in forecast mode (WRF-GFS). By applying the new metric and a correlation coefficient of 0.6 as the lower limit for the skill in the simulations at a height of 126 m, corresponds to a lead time of ≈4 hours (reanalysis) and ≈3 hours (forecast) for both wind speed and direction for turbulence limited lead times. This value is larger than typically found over land – being ≈2 hours. The difference likely relates to the marine conditions of the measurement site. For large lead times, when the simulations are nudged towards the reanalysis the forecast skill does not deteriorate for increasing lead times. This is in contrast to simulations nudged towards meteorological forecasts where the predictability is limited by the non-linearity of the Navier-Stokes equations and a correlation coefficient less than 0.6 was found for lead times larger than ≈6 days for wind speed and somewhat smaller – ≈4 days for the wind direction when applying the new metric. Thus, the window of predictability of the WRF simulations nudged towards a forecast is found to be in the interval ≈4 hours up to ≈6 days (wind speed) and ≈3 hours to ≈4 days (wind direction). These numbers refer to a height of 126 m. The predictive skill is found to be a function of height; at 626 m it is better than at 126 m for both wind speed and direction. For the traditional metric a correlation of less than 0.6 was realized for a lead time larger than ≈4 days for both wind speed and direction.

中文翻译:

使用由预测和再分析数据驱动的中尺度模型研究海上风的可预测性

大气在小和大的时间和空间尺度上的确定性数值天气预报模型本质上是不可预测的,其中一些中间状态已经证明了可预测性;本研究仅涉及时间尺度。较小时间尺度上的混沌性质主要由湍流引起,而在大尺度上则由 Navier-Stokes 方程的非线性引起。我们基于在北海 FINO3 研究平台上使用风激光雷达进行的观测,调查了天气研究和预测模型 (WRF) 提前模拟观测变化的能力。模拟以两种方式进行。在一种类型中,模型使用来自再分析数据集 (WRF-ERA) 的边界条件。或者,模拟是使用来自预测 (WRF-GFS) 的边界条件进行的。本研究的重点是风速和风向变化的可预测性。建议使用一个主要考虑风速和风向(包括湍流结构)逐点变化的度量。然而,为了完整性,还应用了直接比较预测和观察到的风速和风向的传统度量。该指标不会像新指标那样反映小提前期的湍流结构。传统指标显示了非常好的技能(图 2),预测模式 (WRF-GFS) 的模拟提前期长达 4 天。通过应用新指标和 0.6 的相关系数作为 126 m 高度模拟中技能的下限,对应于 ≈4 小时(再分析)和 ≈3 小时(预测)的提前期,对于湍流限制提前期的风速和风向。该值大于通常在陆地上发现的值 - ≈2 小时。差异可能与测量地点的海洋条件有关。对于大的提前期,当模拟被推向再分析时,预测技能不会随着提前期的增加而恶化。这与推动气象预测的模拟形成对比,其中可预测性受到 Navier-Stokes 方程非线性的限制,并且发现相关系数小于 0.6,因为风速的提前期大于 ≈6 天,稍小一点——应用新指标时,风向需要 ≈4 天。因此,推向预测的 WRF 模拟的可预测性窗口被发现在 ≈4 小时到 ≈6 天(风速)和 ≈3 小时到 ≈4 天(风向)的间隔内。这些数字指的是 126 m 的高度。预测技能被发现是身高的函数;在 626 m 处,风速和风向都优于 126 m。对于传统指标,对于风速和风向而言,对于大于 ≈4 天的前置时间,实现了小于 0.6 的相关性。
更新日期:2020-08-04
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