Using machine learning to enhance visibility predictions at Zurich Airport

Airport operations, including runway capacity, delays, and aircraft handling, are tightly coupled to weather. Taking preparatory actions for different weather conditions such as changing wind regimes, visibility reductions, and thunderstorms, is a central part of air traffic management. This meteorological predictions that enhance situational awareness and increase plannability.

Within the MeteoSwiss AVIA26 project, new aviation weather products are developed to forecast thunderstorms [1], wind, and visibility conditions using a machine learning (ML) approach. They provide predictions with high timeliness, short update cycles, spatial representativeness, and information on occurrence probability. Insights into how the predictions are visualized, disseminated and communicated to stakeholders in order to support decision making are given by Landmann et al. [2].

In this contribution, we will focus on the development of an ML-based time series model for predicting visibility at Zurich Airport. We employ the Temporal Fusion Transformer (TFT) model [3], which is an interpretable, multi-horizon, attention-based transformer model for time series. Probabilistic visibility predictions are generated at a 10-minute resolution for the first three hours, and an hourly resolution thereafter up to a lead time of 33 hours. The predictors include visibility measurements, other meteorological station measurements, remote sensing data from satellite, and numerical weather model output. Thanks to the near real-time measurements at and near the airport, predictions can be updated every 10 minutes to reflect the ongoing meteorological tendencies. Both vertical and horizontal visibility time series are predicted simultaneously, resulting in consistent information on visibility regimes at the airport.

We investigate the importance of different predictors and optimize the ML model architecture, considering also tree-based models to test the validity of the TFT model for the use case. The performance of the ML-based prediction is compared against the current deterministic forecast, which is generated from post-processed numerical weather prediction and has an hourly granularity. We find a better performance of the ML-based prediction, particularly for relevant visibility thresholds for aviation. Furthermore, the faster update frequency and probabilistic character make it more helpful for planning and decision making in air traffic management.

 

[1] abstract Attinger et al., 2024, submitted to EMS 2024

[2] abstract Landmann et al., 2024, submitted to EMS 2024.

[3] Lim et al., 2021, https://doi.org/10.1016/j.ijforecast.2021.03.012