Purpose

Weather events have a significant impact on airport arrival performance and may cause delays in operations and/or constraints in airport capacity. In Europe, almost half of all regulated airport traffic delay is due to adverse weather conditions. Moreover, the closer airports operate to their maximum capacity, the more severe is the impact of a capacity loss due to external events such as weather. Various weather uncertainties occurring during airport operations can significantly delay some arrival processes and cause network-wide effects on the overall air traffic management (ATM) system. Quantifying the impact of weather is, therefore, a key feature to improve the decision-making process that enhances airport performance. It would allow airport operators to identify the relevant weather information needed, and help them decide on the appropriate actions to mitigate the consequences of adverse weather events. Therefore, this research aims to understand and quantify the impact of weather conditions on airport arrival processes, so it can be properly predicted and managed.

Design/methodology/approach

This study presents a methodology to evaluate the impact of adverse weather events on airport arrival performance (delay and throughput) and to define operational thresholds for significant weather conditions. This study uses a Bayesian Network approach to relate weather data from meteorological reports and airport arrival performance data with scheduled and actual movements, as well as arrival delays. This allows us to understand the relationships between weather phenomena and their impacts on arrival delay and throughput. The proposed model also provides us with the values of the explanatory variables (weather events) that lead to certain operational thresholds in the target variables (arrival delay and throughput). This study then presents a quantification of the airport performance with regard to an aggregated weather-performance metric. Specific weather phenomena are categorized through a synthetic index, which aims to quantify weather conditions at a given airport, based on aviation routine meteorological reports. This helps us to manage uncertainty at airport arrival operations by relating index levels with airport performance results.

Findings

The results are computed from a data set of over 750,000 flights on a major European hub and from local weather data during the period 2015–2018. This study combines delay and capacity metrics at different airport operational stages for the arrival process (final approach, taxi-in and in-block). Therefore, the spatial boundary of this study is not only the airport but also its surrounding airspace, to take both the arrival sequencing and metering area and potential holding patterns into consideration.

Originality/value

This study introduces a new approach for modeling causal relationships between airport arrival performance indicators and meteorological events, which can be used to quantify the impact of weather in airport arrival conditions, predict the evolution of airport operational scenarios and support airport decision-making processes.

中文翻译：

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天气条件对机场到达延误和吞吐量的影响

目的

天气事件对机场到达性能有重大影响，并可能导致运营延迟和/或机场容量限制。在欧洲，几乎一半的管制​​机场交通延误是由于恶劣的天气条件造成的。此外，机场越接近其最大容量运行，由于天气等外部事件造成容量损失的影响就越严重。机场运营期间出现的各种天气不确定性会显着延迟一些进场过程，并对整个空中交通管理 (ATM) 系统造成网络范围的影响。因此，量化天气的影响是改进提高机场绩效的决策过程的一个关键特征。它将允许机场运营商识别所需的相关天气信息，并帮助他们决定采取适当的行动来减轻恶劣天气事件的后果。因此，本研究旨在了解和量化天气条件对机场到达过程的影响，以便对其进行正确预测和管理。

设计/方法/方法

本研究提出了一种方法来评估不利天气事件对机场到达性能（延误和吞吐量）的影响，并为重要天气条件定义运行阈值。本研究使用贝叶斯网络方法将气象报告中的天气数据和机场到达性能数据与预定和实际移动以及到达延迟相关联。这使我们能够了解天气现象及其对到达延迟和吞吐量的影响之间的关系。所提出的模型还为我们提供了导致目标变量（到达延迟和吞吐量）中某些操作阈值的解释变量（天气事件）的值。然后，这项研究根据综合天气性能指标对机场性能进行了量化。特定的天气现象通过综合指数进行分类，该指数旨在根据航空常规气象报告量化给定机场的天气状况。这有助于我们通过将指标水平与机场绩效结果相关联来管理机场到达运营的不确定性。

发现

结果是根据欧洲主要枢纽超过 750,000 次航班的数据集以及 2015-2018 年期间当地天气数据计算得出的。该研究结合了到达过程（最后进近、滑行和挡块）的不同机场运营阶段的延迟和容量指标。因此，本研究的空间边界不仅是机场，还包括其周围的空域，要同时考虑到站排序和计量区域以及潜在的等待模式。

原创性/价值

本研究引入了一种对机场到达性能指标与气象事件之间的因果关系进行建模的新方法，该方法可用于量化天气对机场到达条件的影响，预测机场运营场景的演变并支持机场决策过程。