A decade of research has shown that numerical weather prediction (NWP)-modeled wind speeds can be highly sensitive to the inputs and setups within the NWP model. For wind resource characterization applications, this sensitivity is often addressed by constructing a range of setups and selecting the one that best validates against observations. However, this approach is not possible in areas that lack high-quality hub height observations, especially offshore wind areas. In such cases, techniques to quantify and disseminate confidence in NWP-modeled wind speeds in the absence of observations are needed. We address this need in the present study and propose best practices for quantifying the spread in NWP-modeled wind speeds. We implement an ensemble approach in which we consider 24 different setups to the Weather Research and Forecasting (WRF) model. We construct the ensemble by considering variations in WRF version, WRF namelist, atmospheric forcing, and sea surface temperature (SST) forcing. Our analysis finds that the standard deviation produces more consistent estimates compared to the interquartile range and tends to be the more conservative estimator for ensemble variability. We further find that model spread increases closer to the surface and on shorter time scales. Finally, we explore methods to attribute total ensemble variability to the different ensemble components (e.g., atmospheric forcing and SST product) and find that contributions by components also vary depending on time scale. We anticipate that the methods and results presented in this paper will provide a reasonable foundation for further research into ensemble-based wind resource data sets.

中文翻译：

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通过集成建模方法量化数值天气预报建模的海上风速的灵敏度

十年的研究表明，数值天气预报 (NWP) 建模的风速可能对 NWP 模型中的输入和设置高度敏感。对于风资源表征应用，这种敏感性通常通过构建一系列设置并选择最能根据观测进行验证的设置来解决。但是，在缺乏高质量轮毂高度观测的地区，尤其是海上风区，这种方法是行不通的。在这种情况下，需要在缺乏观测的情况下量化和传播对 NWP 模拟风速的置信度的技术。我们在本研究中解决了这一需求，并提出了量化 NWP 模拟风速传播的最佳实践。我们实施了一种集成方法，其中我们考虑了天气研究和预测 (WRF) 模型的 24 种不同设置。我们通过考虑 WRF 版本、WRF 名单、大气强迫和海面温度 (SST) 强迫的变化来构建集合。我们的分析发现，与四分位距相比，标准差会产生更一致的估计值，并且对于整体变异性而言，它往往是更保守的估计值。我们进一步发现模型传播在更接近表面和更短的时间尺度上增加。最后，我们探索了将总集合可变性归因于不同集合分量（例如，大气强迫和海温产品）的方法，并发现分量的贡献也随时间尺度而变化。我们预计本文提出的方法和结果将为进一步研究基于集合的风资源数据集提供合理的基础。