Abstract

Contrails are the line-shaped clouds that form due to mixing of the aircraft engine exhaust with ambient air, which may persist for several hours in suitable atmospheric conditions. The climate impact of these persistent contrails is currently estimated to be larger than that due to aviation emitted CO₂. A potential near-term and low-cost mitigation of these impacts would be possible through contrail avoidance, which involves re-routing aircraft around ice supersaturated regions. Current forecasting methods for ice supersaturation have been found to be inaccurate when compared to in-situ measurements. In order to enable comparison between persistent contrail observations and forecast data, we develop a contrail height estimation algorithm to augment the results of an existing contrail detection method using GOES-16 ABI data. The height estimation technique uses a convolutional neural network that is trained using a dataset of 3214 contrails collocated with CALIOP LIDAR data. The algorithm outputs a probability density function rather than a single number, to capture predictive uncertainty. The final algorithm achieves a root mean square error (RMSE) of 570 meters. The height estimation approach is applied in multiple case studies in order to illustrate its performance.

Plain-language Summary

Contrails are the line-shaped clouds that form behind aircraft. Under certain conditions, these artificial clouds can persist for multiple hours and have a significant climate impact. Observational data of contrails can help us better understand and mitigate these harmful impacts. Existing methods for locating contrails on satellite images only provide information on the horizontal location of the contrail. We have created a method that, by only using satellite images, can also estimate the height of these contrails. Such estimates of the vertical location of these contrails will improve comparisons between models and observed data. Contrail height estimates can also be used for re-routing aircraft around existing contrail forming regions, which may mitigate a large portion of contrail climate impact.