Contrail altitude estimation using GOES-16 ABI data and deep learning
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amt-17-6145-2024.pdf
Description
Published version
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7.79 MB
Format
Adobe PDF
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cad929f0022aa652e6b3665dcc2be540
Author(s)
Meijer, Vincent R
Eastham, Sebastian D
Waitz, Ian A
Barrett, Steven RH
Date Issued
October 23, 2024
Journal
Atmospheric Measurement Techniques
Publisher
Copernicus GmbH
Citation
Meijer, V. R., Eastham, S. D., Waitz, I. A., and Barrett, S. R. H.: Contrail altitude estimation using GOES-16 ABI data and deep. learning, Atmos. Meas. Tech., 17, 6145–6162.
Version
Final published version
Abstract
The climate impact of persistent aircraft contrails is currently estimated to be comparable to that due to aviation-emitted CO2. A potential near-term and low-cost mitigation option is contrail avoidance, which involves rerouting aircraft around ice-supersaturated regions, preventing the formation of persistent contrails. Current forecasting methods for these regions of ice supersaturation have been found to be inaccurate when compared to in situ measurements. Further assessment and improvements of the quality of these predictions can be realized by comparison with observations of persistent contrails, such as those found in satellite imagery. In order to further enable comparison between these observations and contrail predictions, we develop a deep learning algorithm to estimate contrail altitudes based on GOES-16 Advanced Baseline Imager (ABI) infrared imagery. This algorithm is trained using a dataset of 3267 contrails found within Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) data and achieves a root mean square error (RMSE) of 570 m. The altitude estimation algorithm outputs probability distributions for the contrail top altitude in order to represent predictive uncertainty. The 95 % confidence intervals constructed using these distributions, which are shown to contain approximately 95 % of the contrail data points, are found to be 2.2 km thick on average. These intervals are found to be 34.1 % smaller than the 95 % confidence intervals constructed using flight altitude information alone, which are 3.3 km thick on average. Furthermore, we show that the contrail altitude estimates are consistent in time and, in combination with contrail detections, can be used to observe the persistence and three-dimensional (3D) evolution of contrail-forming regions from satellite images alone.
MIT Department
Massachusetts Institute of Technology. Laboratory for Aviation and the Environment
Massachusetts Institute of Technology. Joint Program on the Science & Policy of Global Change
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.5194/amt-17-6145-2024
