Performance-based Analysis of Aerodrome Weather Forecasts

Simone, Francesco; Gravio, Giulio Di; Patriarca, Riccardo

doi:10.23919/ntca55899.2022.9934004

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…Previously mentioned works utilised meteorological data to train the models; however, it is essential to acknowledge that weather forecasts may not always be accurate, potentially affecting the predictions of the models. In response to this challenge, the authors of [10] conducted a customised performance-based analysis of weather forecast accuracy in accordance with ICAO (International Civil Aviation Organization) standards. The analysis aimed to aid decision makers in managing airport weather forecasting by leveraging insights from past operations.…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling the Impact of Adverse Weather on Airport Peak Service Rate with Machine Learning

Dalmau,

Attia,

Gawinowski

2023

Atmosphere

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Accurate prediction of traffic demand and airport capacity plays a crucial role in minimising ground delays and airborne holdings. This paper focuses on the latter aspect. Adverse weather conditions present significant challenges to airport operations and can substantially reduce capacity. Consequently, any predictive model, regardless of its complexity, should account for weather conditions when estimating the airport capacity. At present, the sole shared platform for airport capacity information in Europe is the EUROCONTROL Public Airport Corner, where airports have the option to voluntarily report their capacities. These capacities are presented in tabular form, indicating the maximum number of hourly arrivals and departures for each possible runway configuration. Additionally, major airports often provide a supplementary table showing the impact of adverse weather in a somewhat approximate manner (e.g., if the visibility is lower than 100 m, then arrival capacity decreases by 30%). However, these tables only cover a subset of airports, and their generation is not harmonised, as different airports may use different methodologies. Moreover, these tables may not account for all weather conditions, such as snow, strong winds, or thunderstorms. This paper presents a machine learning approach to learn mapping from weather conditions and runway configurations to the 99th percentile of the delivered throughput from historical data. This percentile serves as a capacity proxy for airports operating at or near capacity. Unlike previous attempts, this paper takes a novel approach, where a single model is trained for several airports, leveraging the generalisation capabilities of cutting-edge machine learning algorithms. The results of an experiment conducted using 2 years of historical traffic and weather data for the top 45 busiest airports in Europe demonstrate better alignment in terms of mean pinball error with the observed departure and arrival throughput when compared to the operational capacities reported in the EUROCONTROL Public Airport Corner. While there is still room for improvement, this system has the potential to assist airports in defining more reasonable capacity values, as well as aiding airlines in assessing the impact of adverse weather on their flights.

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Section: Literature Reviewmentioning

confidence: 99%

Modelling the Impact of Adverse Weather on Airport Peak Service Rate with Machine Learning

Dalmau,

Attia,

Gawinowski

2023

Atmosphere

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Comparative Analysis of Clustering Algorithms for Seasonal Pattern Classification and Anomaly Detection in Weather

Jadhav,

Nagaralu,

Kadam

et al. 2023

2023 IEEE 11th Region 10 Humanitarian Technology Conference (R10-Htc)

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Analyses of European Terminal Aerodrome Weather Forecasts in 2022 and 2023

Sládek,

Chalupníková,

Schneidrová

et al. 2024

Aviation

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Terminal Aerodrome Forecasts (TAFs) are essential components of aviation meteorology, providing critical information for flight safety and operational decision-making. This study conducts a comprehensive analysis of TAF for European airports during the years 2022 and 2023, leveraging Python functions accessible via a dedicated GitHub repository. The complexity inherent in TAF, characterized by diverse change groups, header formats, and regional variations, presents challenges for accurate interpretation. The analysis focuses on key parameters within TAF, including the count of corrected messages and the frequency and types of change groups. The count of corrected messages serves as a metric for evaluating the quality of service provided, while the examination of change group utilization reveals distinct patterns and tendencies specific to each airport. The findings underscore the significance of regional regulations, meteorologist decision-making, and adherence to International Civil Aviation Organization (ICAO) standards in shaping TAF. The GitHub repository and associated Python functions presented in this study provide valuable resources for meteorologists, researchers, and aviation personnel to conduct in-depth analyses and derive insights from TAF. Ultimately, this study identifies local differences and inconsistencies in the publication of TAF, laying the groundwork for enhancing their consistency and uniformity.

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Performance-based Analysis of Aerodrome Weather Forecasts

Cited by 3 publications

References 11 publications

Modelling the Impact of Adverse Weather on Airport Peak Service Rate with Machine Learning

Modelling the Impact of Adverse Weather on Airport Peak Service Rate with Machine Learning

Comparative Analysis of Clustering Algorithms for Seasonal Pattern Classification and Anomaly Detection in Weather

Analyses of European Terminal Aerodrome Weather Forecasts in 2022 and 2023

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