The reduction in visibility that accompanies fog events presents a hazard to human safety and navigation. However, accurate fog prediction remains elusive, with numerical methods often unable to capture the conditions of fog formation, and observational methods having high false-alarm rates in order to obtain high hit rates of prediction. In this work, 5 years of observations from the Cabauw Experimental Site for Atmospheric Research are used to further investigate how false alarms may be reduced using the statistical method for diagnosing radiation-fog events from observations developed by Menut et al. (Boundary-Layer Meteorol 150:277–297, 2014). The method is assessed for forecast lead times of 1–6 h and implementing four optimization schemes to tune the prediction for different needs, compromising between confidence and risk. Prediction scores improve significantly with decreased lead time, with the possibility of achieving a hit rate of over 90% and a false-alarm rate of just 13%. In total, a further 31 combinations of predictive variables beyond the original combination are explored (including mostly, e.g., variables related to moisture and static stability of the boundary layer). Little change to the prediction scores indicates any appropriate combination of variables that measure saturation, turbulence, and near-surface cooling can be used. The remaining false-alarm periods are manually assessed, identifying the lack of spatio–temporal information (such as the temporal evolution of the local conditions and the advective history of the airmass) as the ultimate limiting factor in the methodology’s predictive capabilities. Future observational studies are recommended that investigate the near-surface evolution of fog and the role of non-local heterogeneity on fog formation.

中文翻译：

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了解和减少观测雾预测中的误报

伴随雾事件发生的能见度降低对人类安全和航行构成威胁。然而，准确的雾预测仍然难以实现，数值方法往往无法捕捉雾形成的条件，而观测方法具有高误报率以获得高预测命中率。在这项工作中，来自 Cabauw 大气研究实验站的 5 年观测被用于进一步研究如何使用由 Menut 等人开发的用于诊断辐射雾事件的统计方法来减少误报。(Boundary-Layer Meteorol 150:277–297, 2014)。该方法针对 1-6 小时的预测提前期进行了评估，并实施了四种优化方案来调整针对不同需求的预测，在置信度和风险之间进行折衷。随着前置时间的减少，预测分数显着提高，有可能实现超过 90% 的命中率和仅 13% 的误报率。总共探索了超出原始组合的另外 31 种预测变量组合（主要包括，例如，与边界层的水分和静态稳定性相关的变量）。预测分数几乎没有变化表明可以使用任何适当的变量组合来测量饱和度、湍流​​和近地表冷却。剩余的虚警期由人工评估，确定缺乏时空信息（例如当地条件的时间演变和气团的平流历史）作为该方法预测能力的最终限制因素。