the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Contrail altitude estimation using GOES-16 ABI data and deep learning
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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Status: open (until 05 Jun 2024)
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RC1: 'Comment on egusphere-2024-961', Ziming Wang, 26 May 2024
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This paper presents the first remote-sensing-based contrail altitude estimation algorithm. Both the image-level model and the cirrus pixel-by-pixel model are developed and compared, with an evaluation of predictive uncertainty and an assessment of the method's accuracy using individual test data and independent flight data. This study offers valuable insights for further assessing the climate impact of contrail cirrus. The paper is well-organized and well-written. I urge its publication in AMT, with some minor comments provided for the authors' consideration.
Specific comments:
Line 40: Please provide the physical explanations for why the infrared channels are used for estimating cloud top altitude.
Figure 7: The plot shows a trend where the CNN generally overestimates contrail altitude compared to the true values from CALIPSO. Are there any potential ideas for this?
Figure 10: The plot here seems to support my impression from Figure 7 that the contrail altitude can be slightly overestimated. During data collocation, you carefully considered the advection of aircraft data due to horizontal wind. Then, contrail ice crystals can sediment, which should theoretically reduce the altitude rather than increase it when compared to the flight data. Are there any reasons behind this discrepancy?
Conclusion: The RMSE is used as the metric to indicate the accuracy of the algorithm, as emphasized in the abstract. Since the developed contrail altitude retrieval method is the next step due to the biased prediction of ice supersaturation vertical extension in contrail avoidance, would it be better to also show the simple mean bias error or mean absolute error for estimating the contrail altitude?
Technical corrections:
Caption of Figure 1: "Zulu" time is equivalent to "UTC" time. However, I'm not sure if it is widely used in this research field. This applies to the entire text to be consistent with the figure.
L90: “a 50km distance of the ground-track of CALIPSO.” I assume it should refer to the supplement S1.
L132: “FlightAware (for times in 2023)”. Eventually it appears not to have been used because the focus was on the years 2018-2022.
L221: “ISS” instead of “ISSRs”.
L273: tends to be over-confident for probabilities between 0.5 and 0.9, as well as between 0.1 and 0.2.
Overall, the excellent work presented in this article is acknowledged.
Citation: https://doi.org/10.5194/egusphere-2024-961-RC1
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