Abstract This study extends the assessment framework proposed by Murakami et al. (1986) for the pedestrian wind environment to a fully probabilistic method by implementing Bayesian modeling. The method quantifies the uncertainties in constructed models based on the measured wind data and the results of the assessment. To model the probabilities of the daily maximum mean wind speed and wind direction, we employed the Weibull distribution and categorical distribution, respectively. The parameters defining the probability distributions were probabilistically modeled using Bayesian techniques. Using the wind data measured at the Meteorological Observatory of Tokyo, we demonstrated the effectiveness of the proposed method. The results showed that the wind direction probability and each parameter of the Weibull distribution could be estimated in the form of a posterior probability density function. Using the constructed models, we predicted the exceedance probability of the daily maximum instantaneous wind speed and evaluated the wind environment index (rank) in a city model. We provided a discrete rank scale in the form of a probability distribution, which enables us to quantify the evaluation uncertainties. Additionally, we clarified the effect of varying the amount of data used for the model construction. The uncertainty of the exceedance probability decreased with the amount of data. When only 1 year of data was used, some evaluation points possibly changed over three ranks. Even when 5-year observation data was used, the evaluated rank of some points varied within the range of uncertainty, thereby highlighting the importance of uncertainty quantification in the wind environment assessment.

中文翻译：

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使用贝叶斯技术开发行人风环境概率评估框架

摘要 本研究扩展了村上隆等人提出的评估框架。(1986) 通过实施贝叶斯建模将行人风环境转化为完全概率方法。该方法根据测得的风数据和评估结果量化构建模型中的不确定性。为了模拟每日最大平均风速和风向的概率，我们分别采用了威布尔分布和分类分布。使用贝叶斯技术对定义概率分布的参数进行概率建模。使用在东京气象台测量的风数据，我们证明了所提出方法的有效性。结果表明，风向概率和威布尔分布的各个参数可以采用后验概率密度函数的形式进行估计。使用构建的模型，我们预测了每日最大瞬时风速的超越概率，并评估了城市模型中的风环境指数（等级）。我们以概率分布的形式提供了一个离散的等级量表，这使我们能够量化评估的不确定性。此外，我们阐明了改变用于模型构建的数据量的影响。超过概率的不确定性随着数据量的增加而降低。当仅使用一年的数据时，某些评估点可能会在三个级别上发生变化。即使使用了 5 年的观察数据，