Quantifying the Efficacy of Weather Forecasting Data for Flight Contrail Optimization

Abstract

Contrail optimization offers an efficient and cost-effective way for aviation to immediately reduce its climate impact. Open-source optimization, wherein the contrail and emission effects are balanced based on meteorological open data, has been presented in previous work. However, prior research overlooks the importance of using forecasting data, as opposed to post-processed reanalysis data. For contrail optimization to be implementable, forecasting data needs to be available at a sufficient quality in the flight planning stage in order to perform the optimization. In this paper, a fully open non-linear optimal control flight optimization is implemented and applied using both forecasting and reanalysis data. A total of 120 days (175.440 flights) of flight data from OpenSky are used in the analysis. We show that forecasts with larger lookahead times (up to 12 hours) are equally effective when compared to more recent forecasts (1 hour lookahead time) for contrail optimization, with equally high accuracy. However, when compared to more accurate post-processed reanalysis data, there are considerable differences in predicted contrails formed. This research shows there is still a long way to go before we can actually implement contrail optimal flight planning.