Supporting weather forecasting performance management at aerodromes through anomaly detection and hierarchical clustering. (1st March 2023) - Patriarca, Riccardo; Simone, Francesco; Di Gravio, Giulio
Highlights: We propose a ML decision support system for aerodrome weather forecasting. An error propensity metric to guide decision making at aerodromes is introduced. Use of anomaly detection with Spectral Residual and Convolutional Neural Network. Use of hierarchical clustering to define groups of similar forecasts. The decision support system accounts for basic parameters and significant changes. Abstract: Weather forecasting is a critical factor for aerodrome and enroute flight operations. Airport decision-makers rely on assessments made by forecasters to ensure operations safety and optimize flight schedule despite potential adverse weather conditions. This manuscript suggests a novel methodology based on Machine Learning to detect forecasting anomalies in historic data, and to rely on them for anticipating potential threats in aerodrome future forecasts. The methodology is fed with historic bulletins from radars and with previous forecasts, which are then processed via an anomaly detection algorithm, and a hierarchical clustering algorithm. While the former algorithm spots anomalous data points, the latter is used to group sets of similar forecasts. The joint usage of the results allows calculating an error propensity metric, which can predict the expected tendency of a certain forecast to be inaccurate. The methodology is meant to enhance decision makers in managing aerodrome weather forecasting, understanding criticalities related to their accuracy levels.