Preprints
https://doi.org/10.5194/egusphere-2024-961
https://doi.org/10.5194/egusphere-2024-961
29 Apr 2024
 | 29 Apr 2024
Status: this preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).

Contrail altitude estimation using GOES-16 ABI data and deep learning

Vincent R. Meijer, Sebastian D. Eastham, Ian A. Waitz, and Steven R.H. Barrett

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 re-routing 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 ABI infrared imagery. This algorithm is trained using a dataset of 3267 contrails found within CALIOP LIDAR data and achieves a root mean square error 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 evolution of contrail forming regions from satellite images alone.

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Vincent R. Meijer, Sebastian D. Eastham, Ian A. Waitz, and Steven R.H. Barrett

Status: open (until 05 Jun 2024)

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Vincent R. Meijer, Sebastian D. Eastham, Ian A. Waitz, and Steven R.H. Barrett
Vincent R. Meijer, Sebastian D. Eastham, Ian A. Waitz, and Steven R.H. Barrett

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Short summary
Aviation's climate impact is partly due to contrails: the clouds that form behind aircraft and which can linger for hours under certain atmospheric conditions. Accurately forecasting these conditions could allow aircraft to avoid forming these contrails and thus reduce their environmental footprint. Our research uses deep learning to identify three-dimensional contrail locations in two-dimensional satellite imagery, which can be used to assess and improve these forecasts.