| 25 Jun 2026
Observing formation and early evolution of contrails formed by IAGOS aircraft using high-resolution LEO satellite imagery
Abstract. Persistent contrails and contrail cirrus are estimated to be a major contributor to the climate impact of aviation. The mitigation of these impacts by means of technological or operational changes benefits from the ability to skillfully model the formation, evolution, and impacts of contrails. Although these models can be evaluated and improved by use of observations of contrails obtained from remote sensing instruments, these comparisons are hindered by uncertainty in the required meteorological data (such as relative humidity) and limitations in the method of observation (such as younger contrails not being observable in geostationary satellite imagery). To address these challenges, we collocate aircraft equipped with in-situ humidity sensors from the IAGOS fleet in high-resolution (10–30 m) satellite imagery obtained by instruments aboard the low Earth orbit Sentinel-2 and Landsat missions. The resulting dataset consists of 543 IAGOS aircraft found in satellite imagery (51 % of which form contrails), which we use to evaluate predictions of contrail formation by the Schmidt-Appleman criterion (SAC) as well as predictions of contrail growth by the CoCiP model. When accounting for uncertainty in the IAGOS measurements of humidity and temperature, we find that the SAC correctly explains 98.3 % of the observations. Disagreement between predictions and observation increases when using meteorological data from the ERA5 reanalysis, with only 92.1 % of the observations being explained correctly. Out of the 195 annotated contrails, 48.2 % of these contrails were found to persist for longer than 10 s (approximately the jet phase) and 8.7 % longer than 120 s (approximately the vortex phase). The relative humidity with respect to ice is found to correlate most strongly with observed contrail lifetime, exhibiting an R2 value of 0.49 with the logarithm of contrail age. The observed horizontal growth during the jet and vortex phases is consistent with previous observations and contrail model results. Although the limited lifetimes of the annotated contrails prevent robust statistical conclusions for the dispersion phase, three example cases show horizontal growth rates consistent with simulations by CoCiP and that of observations in literature. Overall, this study demonstrates the potential of high-resolution LEO satellites to create observational datasets for evaluating and improving models of contrail formation and early evolution.
General comments
The authors develop and apply a new method to observe contrail formation and their early evolution with LEO satellites. This is a welcome step forward, since contrail formation and early stages cannot be observed with geostationary satellties due to their low spatial resolution. Almost two decades ago, Vazquez-Navarro et al. (2009) used LEOS to detect contrails that were then tracked in image series from a geostationary satellite. This might be mentioned in the paper. Here the authors use the observations to check the prediction whether a contrail forms or not (via the Schmidt-Appleman criterion SAC). IAGOS data (in situ) are used as the ground thruth of RHi, while ERA5 reanalyses are used for the remaining met. data. CoCiP is used to simulate the lateral expansion of three of the persistent contrails, for up to 20 min.
Overall, I like the description of method how the contrails are detected and geolocated which seems to be surprisingly difficult. However, the conclusion of this paper and sketch of further work (text after L 593) is not something that I would rate as a very important topic. I think, it is not necessary to check the SAC again, since this has been done in extenso many years ago (there is a figure in the 1999 special IPCC report). There are other interesting problems that I would see as more important, in particular the strange fact that sometimes contrails survive for a while in subsaturated or even quite subsaturated cases. How is this possible? There are some examples in this study and a thorough investigation of these cases would be a better follow-on project.
Another possibility could be to make this method available for further tracking with geostationary images. You should think about it.
Apart from this general comment, I have one major issue (major, because it appears often in the paper), and a couple of minor points.
Major issue:
1) Engine efficiency: This misleading expression is used throughout the paper. It is misleading since it indicates that it is a property of the engines. This is not correct. It is rather a property of the whole system Aircraft. In Schumann's (1996) original paper it is termed "overall propulsion efficiency" and defined as FV/Qm_f, that is it is the driving power of the aircraft divided by the energy per second necessary to maintain this power. Of course, the engine is an important part of the system, but the flying body itself with its drag is important as well. Later in the paper the problem appears that BADA and PS models give differing results for descending aircraft. Perhaps, η is no longer well defined in such a situation, since both F (thrust) and m_f (fuel flow rate) approach zero (I don't know this, however).
Specific comments:
L 26: don't mix up "climate effect" (Delta T, see level rise, etc.) with radiative effects (ERF, RF). What you describe is the latter.
L 39: I suggest to delete "For example" and start the sentence right with "Geostationary".
LL 60-64: This discussion is not entirely clear. There are three possibilities for SAC fulfilled-no contrail observed: either the forecast was wrong, or the forecast is correct but no aircraft flying, or the observation is not good enough.
LL 66-69: The expression "lower performance of the SAC" is misleading. I think the SAC itself rests on firm thermodynamic grounds. So if forecast and observations don't agree, the error must be somewhere either in the input data for the SAC or in the measurements, but not in the SAC.
Sect. 2.1: when I read that first, I thought that some info on the different channels (i.e. for what they are good) would be nice. This info is then given later, which is ok. Perhaps you can indicate here, that this info will follow.
L 147: What is a "first-difference standard deviation"?
L 210: Please correct. According to Schumann (1996) T_LM is in °C.
L 220: The word "can" surprised me. Perhaps this can be made more concrete (say for the 250 hPa level).
Eq. 6: Please check the units. They don't combine to a length.
L 235: the effective time scale is not defined.
L 244: What effect does the angle between the flight direction and the swath direction have.
LL 247 ff: It remains unclear how the IAGOS data are used within CoCiP. The problem is that IAGOS has a 4 s time step, while the other met data that are used as input have a 1 hr time step.
L 353: "we make us" ???
Table 5: I was surprised by the similar magnitude of the TP and TN cases. The probable reason is that this table is on contrail FORMATION, not on contrail persistence. It would be helpful to mention this in the table caption.
LL 437 ff: Is the interpretation correct that there are then 81% cloud free cases and that in the cloudy cases the increase of RHi was not large? This could be mentioned (if true).
LL 445-457: The explanation invoking the difference in spatial resolution of the sensors sounds plausible at first reading, but on further thinking, questions arise. If the resolution is 10 or 30 m, but the typical distance of the vortex centres is, say, 60-70 m, then the resolution should in both cases suffice to detect a contrail. So perhaps there is a different reason, for instance the initial optical thickness of the contrail in relation to the threshold contrast necessary for the sensors to detect anything. As you have the relative humidty, you could perhaps try to estimate the optical thickness.
L 471: It is unclear whether these three points are a subset of the few purple ones or of the orange ones.
Fig. 6b: There is one of the orange bars that does not cross the zero line. Please check. In the last line of the caption there is an incomplete sentence.
L 501: Just a comment. This points to a tremendous small-scale variability of the UT humidity field, isn't it?
LL 525-526: Why this, that is why vortex separation (a horizontal distance)? Contrail spreading in the dispersion regime is mainly due to vertical wind shear. Thus the vertical extension of the contrail at the end of the vortex phase is important. As far as I know this is coded in CoCiP.
Sect. 3.3.3: I find this section with its three examples a bit thin. What is the message of this section? What happens in case 1? What are these regions of sublimation? Or are these short paths where SAC is not fulfilled?
LL 575-581: Please see my comment to LL 445-457.
L 634: alone (not along).
References: some entries have formatting problems: namely Knight et al., Neis et al., Tompkins et al.
Reference:
Vazquez-Navarro, M., Mannstein, H., Kox, S. (2015): Contrail life cycle and properties from 1 year of MSG/SEVIRI rapid-scan images. Atmospheric Chemistry and Physics (15), 8739-8749. doi: 10.5194/acp-15-8739-2015.