Contrail altitude estimation using GOES-16 ABI data and deep learning
<p>The climate impact of persistent aircraft contrails is currently estimated to be comparable to that due to aviation-emitted CO<span class="inline-formula"><sub>2</sub></span>. A potential near-term and low-cost mitigation option is contrail avoidance, which...
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Copernicus Publications
2024-10-01
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| Series: | Atmospheric Measurement Techniques |
| Online Access: | https://amt.copernicus.org/articles/17/6145/2024/amt-17-6145-2024.pdf |
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| author | V. R. Meijer V. R. Meijer S. D. Eastham S. D. Eastham S. D. Eastham I. A. Waitz S. R. H. Barrett S. R. H. Barrett |
| author_facet | V. R. Meijer V. R. Meijer S. D. Eastham S. D. Eastham S. D. Eastham I. A. Waitz S. R. H. Barrett S. R. H. Barrett |
| author_sort | V. R. Meijer |
| collection | DOAJ |
| description | <p>The climate impact of persistent aircraft contrails is currently estimated to be comparable to that due to aviation-emitted CO<span class="inline-formula"><sub>2</sub></span>. 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.</p> |
| format | Article |
| id | doaj-art-e009028e572446c184f2ab29d3e63a98 |
| institution | OA Journals |
| issn | 1867-1381 1867-8548 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Measurement Techniques |
| spelling | doaj-art-e009028e572446c184f2ab29d3e63a982025-08-20T02:09:52ZengCopernicus PublicationsAtmospheric Measurement Techniques1867-13811867-85482024-10-011761456162Contrail altitude estimation using GOES-16 ABI data and deep learningV. R. Meijer0V. R. Meijer1S. D. Eastham2S. D. Eastham3S. D. Eastham4I. A. Waitz5S. R. H. Barrett6S. R. H. Barrett7Laboratory for Aviation and the Environment, Massachusetts Institute of Technology, Cambridge, MA, USAnow at: Faculty of Aerospace Engineering, Delft University of Technology, Delft, the NetherlandsLaboratory for Aviation and the Environment, Massachusetts Institute of Technology, Cambridge, MA, USAJoint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, Cambridge, MA, USAnow at: Brahmal Vasudevan Institute for Sustainable Aviation, Department of Aeronautics, Imperial College London, London, UKLaboratory for Aviation and the Environment, Massachusetts Institute of Technology, Cambridge, MA, USALaboratory for Aviation and the Environment, Massachusetts Institute of Technology, Cambridge, MA, USAnow at: Department of Engineering, University of Cambridge, Cambridge, UK<p>The climate impact of persistent aircraft contrails is currently estimated to be comparable to that due to aviation-emitted CO<span class="inline-formula"><sub>2</sub></span>. 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.</p>https://amt.copernicus.org/articles/17/6145/2024/amt-17-6145-2024.pdf |
| spellingShingle | V. R. Meijer V. R. Meijer S. D. Eastham S. D. Eastham S. D. Eastham I. A. Waitz S. R. H. Barrett S. R. H. Barrett Contrail altitude estimation using GOES-16 ABI data and deep learning Atmospheric Measurement Techniques |
| title | Contrail altitude estimation using GOES-16 ABI data and deep learning |
| title_full | Contrail altitude estimation using GOES-16 ABI data and deep learning |
| title_fullStr | Contrail altitude estimation using GOES-16 ABI data and deep learning |
| title_full_unstemmed | Contrail altitude estimation using GOES-16 ABI data and deep learning |
| title_short | Contrail altitude estimation using GOES-16 ABI data and deep learning |
| title_sort | contrail altitude estimation using goes 16 abi data and deep learning |
| url | https://amt.copernicus.org/articles/17/6145/2024/amt-17-6145-2024.pdf |
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