Virtual Observations to Bridge Meteorological Data Gaps for Airline Operations in the Caribbean Islands

The Caribbean region presents unique challenges for maintaining a reliable METAR network due to its unique geographical characteristics, resulting in crucial weather observations like temperature and pressure becoming unavailable at times. In response to this issue, Tomorrow.io has developed Virtual Observations on Demand (VOD) for commercial aviation. The VOD solution uses Tomorrow.io's proprietary ML technology, incorporating innovative assimilation techniques to combine historical observations and forecast models. This provides accurate temperature and pressure data for specific locations in the Caribbean. This study focuses on applying VOD to enhance missing METAR data availability for airline operations in the region. Data analysis for ten key stations over 30 months (2020-2022) revealed significant data gaps ranging from approximately 1.2% to 58.9%.

To build a robust ML model, data from the first two years (2020-2022) was used for ML model training, while the third year (2023) was used for forecast validation. Regression models like Random Forest Regressor (RF), Lasso Regression, SVM Regressor, and XgBoost Regressor were combined to improve the accuracy. A Voting Regressor, an ensemble method, integrated predictions from the above four models for a unified and reliable approach. The findings highlight VOD's importance in bridging data gaps and improving situational awareness for airline operations. By analyzing historical weather observations and utilizing ML models, VOD accurately predicts temperature and pressure at specific locations even without direct METAR data.

The VOD methodology generates simulated temperature and pressure data valid for up to 1 hour. Initial validation results show that the MSE values for both temperature (ranging from 0.52 to 1.24) and pressure (ranging from 0.15 to 0.47) predictions varied significantly among the ten METAR locations. Some stations had lower MSE values, indicating higher prediction accuracy, while others had higher MSE values, suggesting less accurate forecasts. Understanding the factors contributing to these differences could lead to the development of more reliable weather forecasting systems overall. By blending historical observations and forecast models, VOD provides airline operators with accurate Virtual Observations, enabling informed decision-making and risk mitigation without direct METAR observations. As a result, VOD not only improves METAR data availability but also enhances airline operations safety and efficiency.

Keywords: Virtual Observations on Demand (VOD), METAR Data, Airline Operations, Caribbean Region, Tomorrow.io, ML Technology, Risk mitigation