# Combining LIDAR, all-sky camera, and ECMWF-ERA5 reanalysis to investigate contrail formation and evolution over Clermont-Ferrand, France on June 2, 2023

Sidy Diarra, Jean-Luc Baray, Nadège Montoux, Patrick Fréville, Frédéric Peyrin, Philippe Cacault, William Boucher, Philippe Keckhut

► To cite this version:

Sidy Diarra, Jean-Luc Baray, Nadège Montoux, Patrick Fréville, Frédéric Peyrin, et al. Combining LIDAR, all-sky camera, and ECMWF-ERA5 reanalysis to investigate contrail formation and evolution over Clermont-Ferrand, France on June 2, 2023. Atmospheric Research, 2026, 329, pp.108500. 10.1016/j.atmosres.2025.108500. hal-05289903

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# Combining LIDAR, all-sky camera, and ECMWF-ERA5 reanalysis to investigate contrail formation and evolution over Clermont-Ferrand, France on June 2, 2023

Sidy Diarra<sup>a,b,*</sup>, Jean-Luc Baray<sup>a,c,*</sup>, Nadège Montoux<sup>a</sup>, Patrick Fréville<sup>c</sup>, Frédéric Peyrin<sup>c</sup>, Philippe Cacault<sup>c</sup>, William Boucher<sup>a</sup>, Philippe Keckhut<sup>b</sup>

## ARTICLE INFO

Keywords:

Contrail

Water vapor

Supersaturation

Climate change mitigation

## ABSTRACT

Contrails formed by aircraft in the upper troposphere contribute to anthropogenic climate forcing. However, the conditions driving their formation and persistence remain incompletely understood. This study combines a ACTRIS/EARLINET ground-based LIDAR, all-sky camera imagery, ADS-B aircraft tracking, and ECMWF-ERA5 reanalysis to analyse contrails formation and evolution over Clermont-Ferrand, France, on June 2, 2023. Twelve contrails are documented throughout the day, including five persistent and seven non-persistent con- trails. Persistent contrails formed at 10.36 km altitude or lower are observed under ice-supersaturated conditions (relative humidity with respect to ice, RHi > 105 %) and at temperatures between 217 and 223 K. Non-persistent contrails produced by higher altitudes aircrafts, are associated with lower RHi mostly below 100 % and colder temperatures (214–217 K). The horizontal persistent contrail widths range from 0.53 ± 0.10 to 1.60 ± 0.44 km (all-sky camera estimation) and 0.35 ± 0.14 to 1.90 ± 0.32 km (LIDAR estimation), and vertical extents varied from 340 ± 10 to 440 ± 20 m. The optical properties of these contrails have also been estimated by LIDAR. Aerosol backscatter coefficient vary from 0.02 to 0.05 km⁻¹ sr⁻¹, scattering ratios from 8 to 20, volume linear depolarization from 0.13 to 0.24 and particle linear depolarization from 0.17 to 0.45. The maximum contrail observation duration by camera is 180 min. The study highlights the potential of ground-based remote sensing for contrail monitoring.

## 1. Introduction

Aircraft can induce the formation of ice clouds in the upper tropo- sphere, typically between 8 and 13 km in altitude, known as contrails (condensation trails). Following their formation, they may develop into visually resembling natural cirrus clouds with comparable microphys- ical properties (Heymsfield et al., 2010). The temperature, humidity, and wind patterns have an impact on this process. Contrails affect the global increase in cloud cover (Kärcher, 2018; Sanogo et al., 2024) constituting an anthropogenic cooling effect impacting the radiative balance of the Earth (Kärcher, 2018; Lee et al., 2021; Schumann et al., 2021).

Contrails also trap outgoing terrestrial radiation and on average, the warming effect is dominant to the cooling effect, resulting in a positive

contribution to global warming (Lee et al., 2021).

The net radiative forcing of contrails depends on factors such as at- mospheric conditions, but also as age and optical properties of the contrails. The mean estimate of their effect on global climate remains uncertain. In 2018, the effective radiative forcing of contrails was esti- mated in a range between 17 and 98 mW m⁻² (Lee et al., 2021). In the future, an increase in radiative forcing is expected. It could reach 160 mW m⁻² by 2050 (Singh et al., 2024). The non-CO₂ radiative forcing attributed to aviation exceed that of CO₂ emissions, which is estimated between 31 and 38 mW m⁻² (Lee et al., 2021). The global annual mean radiative forcing attributable to contrail cirrus is three times larger than the effective radiative forcing from aviation’s cumulative CO₂ emissions.

The urgent need to understand and mitigate the climate impacts of aviation is currently of interest to the scientific community. Several

<footnote><sup>a</sup> LaMP, UMR6016, CNRS, Université Clermont Auvergne, Clermont-Ferrand 63100, France</footnote>

<footnote><sup>b</sup> LaTMOS, UMR8190, CNRS, Université Versailles St Quentin, Guyancourt 78280, France</footnote>

<footnote><sup>c</sup> OPGC, UAR833, CNRS, Université Clermont Auvergne, Clermont-Ferrand 63100, France</footnote>

<footnote>* Corresponding authors at: LaMP, UMR6016, CNRS, Université Clermont Auvergne, Clermont-Ferrand 63100, France.</footnote>

<footnote>E-mail addresses: sidy.diarra2@doctorant.uca.fr (S. Diarra), J.L.Baray@opgc.fr (J.-L. Baray).</footnote>
recent and current international projects aim to study and predict contrail formation, and to develop mitigation strategies to reduce their climatic impact. Notable examples of these initiatives include:

* **BECOM (Better Contrail Mitigation):** This project proposes to develop strategies to mitigate the formation and impact of contrails. By combining advanced atmospheric modelling with real-world flight data, it aims to identify optimal flight paths and operational practices that minimize contrail formation ("BECOM website", 2025).

* **CICONIA (Cracking the non-CO₂ conundrum):** This project proposes to quantify the climate impact of the non-CO₂ emissions and develop mitigation strategies that can be integrated into air traffic management systems ("CICONIA website", 2023).

* **CONTRAILS: Led by the German Meteorological Service (DWD), this project focuses on studying contrail formation and their climate effects ("CONTRAILS website", 2025).**

* **CLIMAVIATION:** This French initiative brings together researchers, industry experts, and policymakers to tackle the climate challenges posed by aviation. Climaviation focuses on developing strategies that address both CO₂ and non-CO₂ emissions, including contrails to achieve a more sustainable aviation sector ("CLIMAVIATION website", 2025).

The contrail formation is intrinsically linked to the emission of water vapor and aerosols by aircraft engines. Atmospheric conditions also play a significant role: for instance, contrails are more likely to form in the upper troposphere in cold and humid air. Meanwhile, aircraft-specific factors, such as the type of fuel used and the efficiency of the engine, also influence contrail formation. Engines that emit more water vapor and particles, or those that are less efficient, are more likely to produce contrails. This phenomenon is governed by the Schmidt-Appleman criterion, which outlines the specific conditions required for contrail formation. This criterion takes into account both atmospheric and aircraft-specific parameters (Heymsfield et al., 2010) and can be used in theoretical contrail formation prediction models (Sun and Roosenbrand, 2023).

For contrails to persist beyond the initial vortex phase, during which wake turbulence and vortices disturb the newly formed contrail, the ambient environment must be supersaturated with respect to ice (RHi > 100 %) (Iwabuchi et al., 2012). A contrail is generally classified as persistent if it remains visible for more than 10 min (Kärcher, 2018).

In most cases, contrails dissipate quickly due to adiabatic heating, which induces sublimation of ice particles (Lewellen and Lewellen, 2001; Unterstrasser et al., 2017). This process typically occurs when the surrounding air is not sufficiently humid to maintain the ice crystals.

However, under certain atmospheric conditions, contrails can persist and even spread to form extensive cirrus-like clouds. This occurs when the atmosphere is supersaturated with respect to ice (RHi > 100 %), (Jensen et al., 1998). The statistical analyses of cirrus clouds observed by LIDAR revealed different clusters based on their morphological behaviour (Keckhut et al., 2006; Mandija et al., 2024). In addition to standard thick clouds (2–3 km), some thinner clouds (< 1 km) are detected quite frequently, with an occurrence of 30 %, including potentially contrails and cirrus. Although research on the microphysical properties of young contrails remains limited, several airborne measurement campaigns have nonetheless provided valuable initial insights into their macroscopic characteristics:

* **FIRE (First ISCCP Regional Experiment):** Conducted in Kansas, USA, during the fall of 1991, this campaign focused on studying cloud properties and provided early data on contrail formation and evolution (Sassen, 1997).

* **ARM (Atmospheric Radiation Measurement):** Held in Oklahoma, USA, in April 1994, this campaign included in situ measurements and contrail spatial dimensions (Sassen, 1997).

* **CONCERT (Contrail and Clouds Experiment):** Two campaigns conducted in the fall of 2008 and 2011 provided further data on contrail characteristics, including detailed measurements of microphysical properties (Chauvigné et al., 2018; Voigt et al., 2010).

* **ML-CIRRUS (Midlatitude Cirrus experiment):** Conducted over southern Germany in March and April 2014, this campaign was among the first scientific missions to utilize the HALO Research Aircraft. It aimed to achieve six main objectives, including the investigation of differences between anthropogenic and natural cirrus (Voigt et al., 2017).

These campaigns have contributed to our understanding of contrail formation and persistence by offering in situ measurements that complement satellite observations. Additionally, ground-based instruments, such as LIDAR and sky-camera, are used for the validation of contrail models (Mannstein et al., 2010; Rosenow et al., 2023; Schumann et al., 2013).

In particular, ground-based cameras offer a low-cost alternative to satellites. They provide high temporal and spatial resolution, enabling continuous monitoring of the formation and evolution of young contrails. These cameras are also capable of detecting optically thin contrails, which can be missed by other observation methods (Mannstein et al., 2010). Recently, Gourgue et al. (2025) published an annotated dataset of images provided by the hemispheric camera of the SIRTA site, near Paris. This dataset is similar to the image dataset produced by the camera of Clermont-Ferrand, and highlights the value of high-frequency ground-based imaging for contrail monitoring.

The present study is part of the work package "Operational and new measurements and characterization" of the European project BECOM. Its aim is to document the observation and formation conditions of contrails through a dedicated case study. To achieve this, we integrate a suite of co-located and complementary observations, including an all-sky camera, a LIDAR (Baray et al., 2020), an aircraft passage information from the ADS-B (Automatic Dependent Surveillance - Broadcast) system (Peyrin et al., 2023), and meteorological, thermodynamic, and dynamic data from ECMWF (European Centre for Medium-Range Weather Forecasts) ERA5 reanalysis (Hersbach et al., 2020). This comprehensive approach allows us to analyse, in a preliminary sampling set, the atmospheric conditions governing contrail formation and persistence, as well as to examine aircraft passages both with and without contrail formation, over the course of a full day.

More specifically, the study has the following objectives:

* Investigate the level of supersaturation necessary for contrail formation, taking into account the nonlinear relationships between temperature (T) and relative and specific humidity (RH and q).

* Establish the atmospheric conditions required for contrails to persist beyond a few minutes, providing insights into the factors that contribute to their longevity.

* Exploit LIDAR and camera data to assess the vertical and horizontal extent of the contrails observed on this day, offering a detailed spatial analysis of their distribution.

This paper is structured as follows: Section 2 describes the dataset used in the study. Section 3 outlines the methodology employed. Section 4 presents the observed contrail cases, identifies the aircraft and shows their observation by the LIDAR. Section 5 focuses on the parameters influencing contrail formation and persistence and give their optical and geometrical properties. Finally, Section 6 summarizes the main conclusions and discussions of the future plans to further investigate this topic.
## 2. Instruments and data

### 2.1. All-sky camera

An EKO SFR-02 all-sky camera has been in operation in Clermont-Ferrand (45.761°N,3.111°E, 420 m a.s.l.) since 2015. It is based on a compact Canon camera equipped with a 180° fisheye lens. It captures images at a resolution of 1024 × 768 pixels every two minutes, with an adjustable acquisition frequency according to user requirements. The system includes a pump with a drying cartridge integrated to prevent condensation under the protective dome. The camera is connected to the network via an Ethernet interface, enabling automated image recording and real-time data transmission.

Since the camera is not equipped with an infrared sensor, it is configured to take images only between sunrise and sunset. In addition, the absence of a solar mask in the system may impact the quality of photos taken during intense sun radiation. The captured images are processed using the code ELIFAN (Lothon et al., 2019) to calculate cloud fraction.

The spatial coverage of the camera for detecting aircraft and contrails is not easy to determine due to the optical distortion caused by the fisheye lens. Experiments described in Section 3.2 have shown a field of view with negligible distortion of ±27°, corresponding to a radius of ~10 km at an altitude of 10 km, with a pixel size of 46.75 m at this altitude. Distortion increases and horizontal resolution decreases with distance from the camera's zenith. By looking for aircraft visible at the edges of the full-sky camera and using the image timestamp to retrieve their positions from the ADS-B data records (see Section 2.4), aircraft can sometimes be observed up to 70 km away, with an uncertainty of 15 km, assuming a mean aircraft speed of 900 km h⁻¹ and a time uncertainty of 1 min (the interval between two successive images). However, at these distances, estimating the contrails' widths would not be possible without very large uncertainty due to the horizontal resolution.

### 2.2. LIDAR COPLid

A LIDAR system has been in operation in Clermont-Ferrand since 2008. Initially, it was a single-wavelength (355 nm) Raymetrics LIDAR equipped with a Quantel CFR-400 laser emitting at 355 nm. This system provided continuous vertical profiles of Rayleigh-Mie backscattering in the troposphere and lower stratosphere, along with a tropospheric water vapor Raman channel (Fréville et al., 2015).

In 2022, the LIDAR system was upgraded to include a three-wavelength configuration (355, 532 and 1064 nm), being then referred to as COPLid (Cézeaux Opme Puy de Dôme LIDAR). This upgrade includes a more powerful Spectra Physics INDI laser emitting with a pulse repetition rate of 10 Hz. The signal reception is handled by a 40 cm diameter Cassegrain telescope with a 4 m focal length. The wavelength and polarization separation unit (WSU) was redesigned to incorporate the new channels, notably the addition of parallel and perpendicular polarization separation at 532 nm and a dedicated 1064 nm channel. In accordance with the ACTRIS (Aerosol, Clouds, and Trace Gases Research Infrastructure) quality assurance standards, the WSU is positioned on a rotating stage to aid in the polarization calibration (Wandinger et al., 2020).

The system operation is now fully automated, incorporating rain and wind sensors, infrared barriers to detect human intrusions, and an ADS-B system to monitor air traffic which stops the operation if an aircraft passes overhead as described below in Section 2.4 (Peyrin et al., 2023). The operation is supervised by a software developed at the OPGC (Observatoire de Physique du Globe de Clermont-Ferrand) observatory using LabVIEW, which autonomously manages all LIDAR measurement processes without the need for human intervention. The data provided by the LIDAR has a vertical resolution of 7.5 m and an integration time set to 1 min (600 laser shots).

### 2.3. ACTRIS/EARLINET Single Calculus Chain (SCC) LIDAR data processing

The European Aerosol Research LIDAR Network (EARLINET) is a European research initiative dedicated to the LIDAR observation of atmospheric aerosols across the European continent (Pappalardo et al., 2014). To ensure a standardized processing of raw LIDAR data collected from various stations, EARLINET has developed the Single Calculus Chain (SCC), a community code designed for uniform data inversion procedures (Papagiannopoulos et al., 2020).

The SCC has two dedicated modules to produce optical parameters (Level 2) from raw data (Level 0):

* ELPP (EARLINET LIDAR Pre-Processor): This module performs the pre-processing of raw LIDAR signals, applying essential corrections such as dead-time adjustment, trigger-delay correction, and background noise subtraction on the raw signals before calibration (D'Amico et al., 2016).

* ELDA (EARLINET LIDAR Data Analyzer): This module generates vertical profiles of aerosol optical properties, including extinction and backscatter coefficients (Mattis et al., 2016).

The SCC incorporates an error analysis which provides the absolute statistical uncertainty of all variables using the standard error propagation (Taylor, 1997), and Monte Carlo simulations assuming Gaussian distributions for analog signals and Poisson distributions for photon-counting signals. Additionally, the SCC optimizes the vertical and temporal resolutions of LIDAR products while maintaining rigorous detection of low-level clouds.

In this study, we use the products generated by the SCC to determine the scattering ratio of persistent contrail signals. Specifically, the ELPP module provides the following variables:

* Calculated molecular extinction at the emission wavelength (m⁻¹) which represents the attenuation of the laser beam due to molecular scattering and absorption along its path.

* Calculated molecular LIDAR ratio at the emission wavelength (sr) which is the ratio between the molecular extinction and the molecular backscatter coefficients.

Additionally, the ELDA module calculates the following variables:

* Aerosol backscatter coefficient (m⁻¹ sr⁻¹) which quantifies the amount of laser light backscattered by aerosol particles in the atmosphere per unit distance and per unit solid angle.

* Volume linear depolarization ratio (dimensionless units) which is the ratio of the cross-polarized to co-polarized backscattered signals. This parameter indicates changes in the polarization state of the laser beam after being scattered by atmospheric molecules and particles.

* Particle linear depolarization ratio (dimensionless units) which is similar to the volume linear depolarization ratio but limited to the backscatter by aerosol and cloud particles, excluding the molecule contributions. It is a key indicator of particle non-sphericity and phase.

These parameters are provided at the LIDAR's native resolution, which is 7.5 m. However, the SCC processing may introduce some smoothing in the profiles. For this study, the configuration was optimized to minimize smoothing, as the highest possible spatial resolution was required. At contrail altitudes, no smoothing was applied, meaning that the raw 7.5 m resolution was preserved, except for a few isolated bins located outside the contrail layers, where a 3-bin smoothing (i.e. 22.5 m) was applied to maintain the signal-to-noise ratio above the SCC threshold. In this study, we assume an extinction-to-backscatter LIDAR ratio of 25 sr, a value commonly adopted for contrails and thin cirrus clouds (Iwabuchi et al., 2012). The scattering ratio is calculated as the
ratio of the total (aerosol and molecular) backscatter coefficient to the molecular backscatter coefficient, where the molecular backscatter coefficient is defined as the ratio of the molecular extinction at the emission wavelength to the molecular LIDAR ratio at the same wavelength. The uncertainty of optical parameters depends on the quality of the system calibration performed prior to the measurements and on the accurate characterization of the optical components. Both calibration tests and optical specifications are taken into account by the SCC during the processing of the measurements.

## 2.4. Automatic Dependent Surveillance-Broadcast (ADS-B) system

ADS-B is a surveillance technology used in aviation to track and share real time information such as the position, speed, and other flight parameters of aircraft. Airplanes and helicopters are equipped with transponders that transmit data to ground-based receivers, such as the one integrated into the LIDAR COPLid acquisition system. This ADS-B system was installed on COPLid in 2019 to ensure compliance with aviation safety requirements by automatically halting laser emission when an aircraft is detected in proximity. The system is designed to ensure minimal disruption in data acquisition, facilitating the most continuous possible monitoring of the atmosphere, with particular attention to the effects of aviation on atmospheric composition. Full technical details of the installation are provided in Peyrin et al. (2023).

For this study, we use ADS-B records of aircraft with a specific configuration capable of detecting aircraft within a 100 km radius. The data included parameters such as latitude, longitude, altitude, distance from the LIDAR, distance between the aircraft and the camera, and aircraft type. This information is used to correlate observed contrails with the aircraft responsible for their formation. In addition to the aircraft type, the real time cruise altitude is a critical useful parameter to inform about the altitude of gas exhaust.

## 2.5. ECMWF-ERA5 reanalysis

Atmospheric reanalyses are a reliable source of meteorological parameters. ERA5 is the fifth-generation atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). This global model provides detailed estimates of past and present atmospheric conditions at the global scale. ERA5 is generated using a numerical atmospheric model that assimilates a wide range of real-time meteorological observations, including satellite, airborne, and terrestrial data such as radiosondes and dropsondes (Hersbach et al., 2020).

This study is based on thermodynamic and dynamic parameters (temperature, air density, horizontal and vertical wind), and water vapor content (relative and specific humidity). The data are extracted at a horizontal resolution of 0.25° and a temporal resolution of one hour. The associated uncertainties are obtained from the ensemble spread, which is the standard deviation of forecasts generated using a probabilistic ensemble approach. This approach includes multiple perturbed members and an unperturbed forecast, known as the control member. To ensure maximum vertical resolution, we used the 137 native model levels rather than the interpolation on pressure levels. This requires recalculating relative humidity based on saturation vapor pressure and specific humidity, following the formula used in ECMWF-ERA5 reanalysis. Relative humidity is related to both ice and liquid water, depending on the temperature:

* For $T < -23$ °C, the relative humidity is calculated with respect to ice (RHi), according to Alduchov and Eskridge (1996).

* For $T > 0$ °C, the relative humidity is calculated with respect to liquid water (RH), according to Buck (1981).

* For $-23$ °C $< T < 0$ °C, a linear combination of the two is used.

Humidity and cloud distributions in the ERA5 reanalysis have been

greatly improved compared to earlier reanalyses (e.g., ERA-Interim), thanks to the higher resolution, the assimilation of a broader range of observations, and the use of a more recent version of the ECMWF Integrated Forecasting System, which introduced updated dynamical core and improved physical parameterizations of clouds and precipitation, that strongly influence humidity. Nevertheless, Typical errors and biases have been reported, indicating that ERA5 exhibits a slight dry bias relative to observations (radiosondes, IAGOS, LIDAR DIAL) in the upper troposphere and lower stratosphere (Wolf et al., 2025). More recently, a machine learning approach based on artificial neural networks has been proposed to improve the representation of relative humidity over ice in ECMWF numerical weather prediction models (Wang et al., 2025).

# 3. Methodology

## 3.1. General approach

The systems used to investigate contrails provide a complementary information set. In the case of few water clouds, the full sky camera can identify white crystal ice contrails signatures around the LIDAR, due to wave matter interaction with the solar light.

The ADS-B system provides information about aircraft types, their individual pathway and mainly their cruise altitude that cannot be deduced easily by the camera images. The real time LIDAR observations during the contrail formation is not expected to occur while the LIDAR field of view is rather small and the probability to have cruise pathway above the LIDAR is small and impossible for security reasons. However the already formed contrails advected horizontally by the wind are often crossing the laser beam of the LIDAR allowing to observe vertical extends and horizontal spreading of the clouds using wind information. The thermodynamic information is provided by the ECMWF-ERA5 meteorological analyses.

More specifically, the methodology employed to document contrails in this study consists of the following steps:

* Identification of persistent and non-persistent contrails using images captured by the all-sky camera.

* Determination of the aircraft responsible for the contrails and associated information about its position by analysing data from the ADS-B system.

* Analysis of contrail signatures in COPLid LIDAR signals to infer their optical properties and their geometrical extension. Comparison with horizontal extents estimated with the camera.

* Documentation of atmospheric conditions influencing the formation and persistence of contrails, including thermodynamic and dynamic parameters.

The day June 2, 2023 has been selected as a case study due to favourable observational conditions. The instruments (LIDAR and camera) were fully operational, and the sky was free of low clouds that could have disrupted the observations. On this day, a total of 12 contrails were observed as illustrated in Fig. 1. In the rest of this paper, these 12 contrails are identified as C1 to C12.

## 3.2. Contrail horizontal extent estimation

In this study, the contrail width has been estimated using two distinct and independent approaches: (1) full-sky camera imagery and (2) LIDAR measurements combined with horizontal wind from the ECMWF ERA5 reanalysis.

The image-based method is based on optical calculations under Gaussian conditions. This means that the magnification does not change as a function of the field angle. Consequently, the effects of distortion do not require correction. The method involves several processing steps and its application to contrails C1 to C5 is shown in Fig. 2:
<figure><img src="image_3.png" /><figcaption>Fig. 1. Observations of different contrails photographed at the moment of their formation by the all-sky camera above Clermont-Ferrand on June 2, 2023. The annotations C1 to C12 refer to the different contrails described in section 4. The times of passage of the aircraft that formed these contrails are: C1 4:20, C2 4:50, C3 5:04, C4 5:30, C5 5:34, C6 4:54, C7 4:59, C8 5:34, C9 6:04, C10 13:35, C11 13:43, C12 14:32 UTC.</figcaption></figure>

* Camera calibration: The effective focal length of the double-lens full-sky camera is determined using a calibration target composed of squares, positioned at a distance of 2.77 m. The effective focal length is thus estimated to be 1.20 ± 0.05 mm.

* Selection of measurement region: The estimated field of view with negligible distortion is ±27°. It is only in this area that contrail width calculations will be made. The contrail width is measured near the centre of the image to coincide with the LIDAR field of view used in the second method.

* Contrail direction determination: The main axis of the contrail and the orthogonal cross-sectional axis (white lines on Fig. 2), which allow to determine the contrail width, are manually identified.

* Width estimation: The contrail width, in terms of pixel count, is finally visually estimated on image in the red channel by analysing the pixel value on the orthogonal cross-sectional axis. We distinguish the length where the contrail is identified with certainty (black segments on Fig. 2), from the length where the signature is less clear (grey segments on Fig. 2) due to contrail dissipation and/or lack of image sharpness or resolution. The contrail width is estimated by the

mean of these two lengths in pixel number. The conversion from a pixel number n to a physical distance w (in m) accounts for the altitude of the aircraft z (in km), the equivalent focal length f (mm) and the size of an image pixel x (µm) according Eq. (1).

$$w = \frac{z \times x \times n}{f}, \qquad (1)$$

* Incertitude analysis: The error estimation on the contrail width Δw was conducted through error propagation according Eq. (2).

$$\Delta w = w \sqrt{\left(\frac{\Delta z}{z}\right)^2 + \left(\frac{\Delta n}{n}\right)^2 + \left(\frac{\Delta f}{f}\right)^2}, \qquad (2)$$

where the uncertainty on the altitude Δz is taken as the absolute difference between the aircraft altitude provided by the ADS-B system and the bottom altitude of the contrail observed by the LIDAR. The uncertainty on the number of pixels Δn is estimated from half the difference
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Red channel pixel values (ranging from 0 to 255) in full-sky camera images, showing a transect crossing contrails C1 to C5, captured when each contrail is near the image centre and close to the LIDAR vertical. Left panels (a, c, e, g and i) show the full images, while right panels (b, d, f, h and j) present a zoomed view of the transects. The white line marks the full transect, with black and grey segments indicating, the sections where the contrail is clearly identified and where its presence is less distinct, respectively. The times of passage of the contrails at the LIDAR vertical are: C1 4:34, C2 5:16, C3 5:28, C4 5:42, C5 5:48 UTC. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

between the grey and black segments pixel numbers and the uncertainty on the focal length $Δf$ has been estimated experimentally to 0.05 mm. No uncertainty is taken for the size of a pixel image deduced from the manufacturer data however, to derive it, we have considered that all the pixels of the CCD camera are used to record the image.

The LIDAR based method (2) estimates the contrail width by multiplying the horizontal wind speed (V in meter per second) by the time (t in second) during which the contrail remains within the LIDAR field of view and by a geometrical correction factor. This factor is due to the fact that the LIDAR doesn't record the contrail according to the
width direction because of the wind. The duration during which a contrail remains within the LIDAR field of view is estimated based on the LIDAR signal profiles that present a detectable contrail signature. Given that the temporal resolution of the LIDAR system is one minute, and that a contrail intersecting the LIDAR beam only partially during the integration period may still produce a detectable signature on the LIDAR signal, the total integration time t is estimated by subtracting one minute from the observed LIDAR detection interval provided in Table 1. This one-minute subtraction accounts for the possibility of partial detection and is subsequently considered as the uncertainty Δt associated with the integration time.

Finally, the contrail width L can be written as:

$$L = V \times t \times \cos(\delta - \theta), \qquad (3)$$

with

$$\theta = \tan^{-1} \frac{|v|}{|u|}$$

and

$$V = \sqrt{u^2 + v^2}$$

where:θ is the angle between the West and the wind direction,u is the zonal wind component (m s⁻¹, positive from west to east),v is the meridional wind component (m s⁻¹, positive from south to north),δ is the angle between the contrail width direction (given by the black segment on Fig. 2) and the East direction.

The uncertainty on the width is estimated by applying error propagation to eq. (3) and give eq. (4):

$$\Delta L = \sqrt{ \frac{(V \times \cos(\delta - \theta) \times \Delta t)^2}{\left( (t \times \left( \frac{|u|}{V} \times \cos(\delta - \theta) - \frac{|v| \times \sin(\delta - \theta)}{V} \right) \times \Delta u) \right)^2 + \left( (t \times \left( \frac{|v|}{V} \times \cos(\delta - \theta) + \frac{|u| \times \sin(\delta - \theta)}{V} \right) \times \Delta v) \right)^2} } \quad (4)$$

with

* Δu and Δv the uncertainties on the wind components u, v obtained from the ECMWF ERA5 ensemble spread as indicated in Section 2.5.

* Δt the uncertainty on the LIDAR integration time t.

* Δδ the uncertainty on the angle δ representative of contrail width direction. This uncertainty is estimated to be 1°.

## 4. Contrail observations, aircraft identification and LIDAR signatures

The all-sky camera began recording usable images on June 2, 2023, at approximately 03:30 UTC. The first images of the day show a clear sky, free of low cumulus clouds or high cirrus clouds. Around 04:08 UTC, a few thin cirrus clouds appear at the edge of the camera image. These clouds are visible in Fig. 1 (C1 to C9), which displays the contrails observed by the camera. The sky remains mostly clear in the morning between 04:00 and 06:30 UTC, allowing for a good observation of the contrails. In the afternoon, between 13:00 and 17:00 UTC, a few low cumulus clouds develop, slightly hindering the contrail observations (C10 to C12).

Among the contrails formed in the morning, C1 to C5 are persistent, whereas C6 to C9 are not (lifetime <3 min). Similarly, the contrails observed in the afternoon (C10 to C12) are non-persistent. To link the observed contrails to the aircraft that generated them, we used the ADS-B module in conjunction with the camera. Fig. 3 illustrates the flight paths of the aircraft responsible for the contrails shown in Fig. 1, and Table 1 provides technical details, including aircraft types, positions, and passage times.

The aircraft responsible for contrails C1 to C12 are flying over the full-sky camera at altitudes between 9.75 km and 11.58 km, the exact altitudes of each flight being given in Table 1. Table 1 indicates also that persistent contrails are associated with aircraft of the same type and equipped with the same engines (Boeing 777, twin-engine). Conversely, non-persistent contrails are linked to different aircraft types with various engine configurations. However, it should be noted that these conclusions reflect only the observations made on the studied day and should not be considered as a general rule. As depicted in Fig. 3, all aircraft followed a similar east-to-west trajectory, except for the one responsible for contrail C10, which travelled north to south.

Fig. 4 presents the backscatter profile measurements recorded by the LIDAR COPLid during contrail observations during the morning of June 2, 2023. The signatures of five persistent contrails (C1 to C5) can be identified between 04:30 UTC and 05:45 UTC.

Additionally, Fig. 4 shows strong backscatter signals after 06:00 UTC, corresponding to cirrus clouds given their altitude and thickness. The signature of C1 appears on the LIDAR profiles at 04:33 UTC, 15 min after the contrail formation. This contrail remains visible on the camera image for 2 h and 40 min before exiting from the image frame.

The signature of C2 appears on the LIDAR profiles at 05:15 UTC, 27

<caption>**Table 1**</caption>

Summary of main aircraft characteristics and all sky camera observations of contrails on June 2, 2023.

<table><thead><tr><th>Contrail</th><th>Observation duration camera (min)</th><th>Aircraft type</th><th>Aircraft motor</th><th>Aircraft altitude (km)</th><th>Time of aircraft passage in the camera field of view (UTC)</th><th>Time of LIDAR signature (UTC)</th></tr></thead><tbody><tr><td>C1</td><td>160</td><td>Boeing 777-224</td><td>2-engine</td><td>10.36</td><td>4:16-4:20</td><td>4:33:15<br>4:44:21</td></tr><tr><td>C2</td><td>124</td><td>Boeing 777-2DZLR</td><td>2-engine</td><td>10.36</td><td>4:46-4:50</td><td>5:15:40<br>5:24:45</td></tr><tr><td>C3</td><td>132</td><td>Boeing 777-36NER</td><td>2-engine</td><td>10.36</td><td>5:00-5:04</td><td>5:27:31<br>5:35:00</td></tr><tr><td>C4</td><td>130</td><td>Boeing 777-31HER-35,597</td><td>2-engine</td><td>9.75</td><td>5:26-5:30</td><td>5:41:04<br>5:45:06</td></tr><tr><td>C5</td><td>106</td><td>Boeing 777-31HER-35,575</td><td>2-engine</td><td>10.36</td><td>5:30-5:34</td><td>5:47:08<br>5:56:13</td></tr><tr><td>C6</td><td>&lt;1</td><td>Airbus A380-842-236</td><td>4-engine</td><td>11.58</td><td>4:50-4:54</td><td>None</td></tr><tr><td>C7</td><td>&lt;1</td><td>Airbus A350-1041</td><td>2-engine</td><td>11.58</td><td>4:55-4:59</td><td>None</td></tr><tr><td>C8</td><td>&lt;3</td><td>Airbus A380-842</td><td>4-engine</td><td>10.97</td><td>5:30-5:34</td><td>None</td></tr><tr><td>C9</td><td>&lt;3</td><td>Boeing 787-9 DRL-39657</td><td>2-engine</td><td>10.97</td><td>6:00-6:04</td><td>None</td></tr><tr><td>C10</td><td>&lt;3</td><td>Airbus A320-214</td><td>2-engine</td><td>10.66</td><td>13:31-13:35</td><td>None</td></tr><tr><td>C11</td><td>&lt;1</td><td>Airbus A330-343</td><td>2-engine</td><td>11.58</td><td>13:39-13:43</td><td>None</td></tr><tr><td>C12</td><td>&lt;3</td><td>Boeing 777-333(ER)</td><td>2-engine</td><td>10.36</td><td>14:28-14:32</td><td>None</td></tr></tbody></table>
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Trajectories of the 12 aircraft that formed contrails above Clermont-Ferrand on June 2, 2023, from ADS-B data. The direction of each flight is from the circle to the cross. The colours are the following: C1 grey, C2 blue, C3 red, C4 purple, C5 light orange, C6 cyan, C7 pink, C8 yellow, C9 orange, C10 green, C11 dark pink and C12 dark green. The red star indicates the position of the LIDAR. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**Fig. 4.** 355 nm photocounting perpendicular polarization backscatter signal measured by the LIDAR COPLid on June 2, 2023 between 4:00 and 6:50 UTC with signatures of contrails C1 to C5.</figcaption></figure>

min after its formation. However, only a small part of the contrail persisted (Fig. 2c), with the majority dissipating within a few seconds after the aircraft's passage. The persistent portion remains on the camera image for 2 h and 4 min before exiting, while still developing. Meanwhile, C1 and C2 are still visible on the camera image. As C2, only a small part of C3 persists a few minutes after its formation which appears on the LIDAR profiles at 05:27 UTC, 25 min after its formation and remains visible for over 2 h and 12 min.

C4 remains visible on the camera image while the other contrails also continue to evolve within the image frame. After 13 min of development, the signature of C4 appears on the LIDAR data at 05:41 UTC. C4 remains in the field of view for 2 h and 10 min before exiting at 07:36 UTC.

C5 appears on the camera image at around 05:30 UTC, with the

corresponding aircraft flying at an average altitude of 10.36 km. Its signature becomes visible on LIDAR profiles 15 min after formation, around 05:47 UTC. It then drifts to the edge of the camera image and merges with other contrails at 07:16 UTC.

The other contrails observed on Fig. 1 on June 2, 2023 (C6 to C12) are non-persistent and no signature is identifiable on the LIDAR data.

C6 has been generated by a four-engine Airbus A380-842 at 04:50 UTC at an altitude of 11.58 km, but the associated contrail dissipates almost immediately. A few minutes later, at 04:58 UTC, a twin-engine Airbus A350-1041 flying at the same altitude did not generate any persistent contrail (C7). Around 05:30 UTC, a four-engine Airbus A380-842 generates C8, which is more visible than C6 and C7 and persists a little longer before dissipating. This aircraft is flying at an altitude of 10.97 km. About thirty minutes later, at 06:00 UTC, a twin-engine Boeing 787-9 generates a non-persistent contrail (C9), at the same altitude, 10.97 km. In the afternoon, between 13:00 and 17:00 UTC, several aircraft are observed, but among the contrails observed (C10 at 13:31 UTC, twin-engine Airbus A320-214, altitude 10.66 km; C11 at 13:39, twin-engine Airbus A330-343 altitude 11.58 km; C12, twin-engine Boeing 777-333(ER) at 14:26 UTC and 14:28 UTC, altitude 10.36 km), none are persistent. These results are generally consistent with Meijer et al. (2024), who estimated contrail altitudes using CALIOP and GOES-16 data and a convolutional neural network approach. They demonstrated that contrails are typically observed between 8 and 15 km in altitude, with a peak occurring between 12 and 14 km. Contrails form at slightly higher altitudes in summer and lower in winter, with an average altitude of approximately 11.1 km in winter and 11.8 km in summer. On June 2, 2023, two factors distinguish aircraft forming persistent contrails from those that did not: altitude and time. The aircraft producing persistent contrails flew at lower altitudes (below 10.36 km) and were observed in the morning, the most favourable time of day for the formation of persistent contrails as shown by Yu et al. (2024) from high-resolution thermal infrared satellite imagery from SDGSAT-1. We therefore propose to investigate the differences in
meteorological conditions between the two.

**5. Analysis of atmospheric conditions and estimation of contrail extensions**

**5.1. Characterization of water vapor supersaturation zones**

In this section, we analyse the relationship between humidity, tem-
perature, and wind patterns with contrail observation and persistence.
The values of these parameters, obtained from the ECMWF-ERA5
reanalysis at the 250 hPa pressure level close 10 km, the typical
aircraft cruising altitudes and contrail formation heights, are presented
in Fig. 5.

The maps at 05:00 and 14:00 UTC are representative of the atmo-
spheric conditions encountered by aircraft in the morning and

afternoon, respectively. In the morning, relative humidity fields reveal a
moist layer with relative humidity values exceeding 118 %, covering the
entire central region of France (Fig. 5a). In the afternoon, this moist
layer is not strongly advected and becomes drier, with values decreasing
below 100 % (Fig. 5b). The temperatures, between 217 and 221 K above
France at this pressure level, follows an inverse trend, slightly increasing
by 0.12 K between 05:00 and 14:00 UTC (Fig. 5c, d).

Fig. 5 (e,f) shows that the intensity of horizontal winds is relatively strong (29 m s⁻¹) over northern and southern France but remains weak in the contrail observation area (6 m s⁻¹). This is consistent with the limited advection of the high-humidity region observed in the morning (Figs. 5a and b). Additionally, at a large scale, the vertical wind remains slightly upward in the morning and downward in the afternoon. Although the magnitudes are very low (less than 0.1 Pa s⁻¹, Fig. 5f), this is in agreement with observations presented by Gierens and Brinkop

<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Isobaric maps of relative humidity (a,b), temperature (c,d) and the 3 components of the wind (e,f) on the 250 hPa pressure level on June 2, 2023, at 05:00 UTC (a,c,e) and 14:00 UTC (b,d,f). The relative humidity isocontours 100, 105 and 110 % are marked by white dotted lines, grey and blue solid lines respectively (a, b). The horizontal wind direction is indicated by arrows whose size indicates the horizontal wind intensity (e,f). The position of the observation point is indicated by a yellow star. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
(2012), showing that upward wind and divergent airflow are favourable conditions for the formation of ice supersaturation, and ice supersaturation is rare in regions with downward or convergent flow.

ECMWF-ERA5 have thus enabled the characterization of the evolution of relative humidity, temperature, and wind distributions between morning and afternoon of June 2, 2023, on a large horizontal scale near the aircraft cruising altitudes. We now focus on the temporal and vertical evolution of supersaturation zones directly above the contrail observation area. To this end, Fig. 6 presents the temporal evolution of the vertical relative humidity profile during June 2, 2023.

Between 00:00 and 01:00 UTC, the entire troposphere remains undersaturated (RH < 100 %). Between 02:00 and 13:00 UTC, the region between 8.9 and 11.1 km becomes supersaturated up to 118 %, before becoming undersaturated again until 17:00 UTC. After 17:00 UTC, supersaturation re-establishes, but with relative humidity values below 105 %. Persistent contrails (C1 to C5) formed within the supersaturated zone (RH > 105 %), whereas non-persistent contrails (C6 to C12) developed in an undersaturated environment (RH < 100 %).

The results on the conditions of formation and persistence of contrails observed in the present study show some similarities but also some differences related to the works previously published (Freudenthaler et al., 1995; Poellot et al., 1999; Voigt et al., 2010). The altitudes of the contrails observed here are consistent with those reported by Voigt et al. (2010), who observed contrails forming between 8.5 and 11.6 km at temperatures ranging from 213 to 229 K during the first CONCERT campaign in Europe. In the present study, contrails are observed between 9.7 and 11.6 km, with temperatures ranging from 217 to 223 K. This confirms that in our study, the atmospheric conditions leading to contrail formation are comparable to the typical mid-latitude atmospheric conditions where commercial aircraft commonly operate.

However, some minor differences can be noted. The contrails observed in the present study persisting for less than three minutes form at slightly lower temperatures, around 210 K. Some variability could be linked to local atmospheric conditions and aircraft engine characteristics. Voigt et al. (2010) reported values of RHi predominantly between 75 % and 105 %, with extremes ranging from 55 % to 122 %. Our observations present a similar distribution, with most contrails occurring within a RHi range between 85 % and 115 %. We recorded RHi maximum values reaching 120 %, that is slightly lower than those observed by Voigt et al. (2010).

<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Temporal evolution of the vertical profile of relative humidity as a function of altitude and pressure level above the observation point (45.76°N, 3.11°E) on June 2, 2023, between 00:00 and 23:00 UTC. The relative humidity isocontours 100, 105 and 110 % are marked by white dotted lines, grey and blue solid lines respectively. The times and altitudes of aircraft that formed persistent contrails C1 to C5 are indicated by white stars, the black stars representing those that did not (C6 to C12). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

## 5.2. Small-scale variability of atmospheric parameters along the contrail

The analysis of temperature, humidity, and wind fields presented in Section 5.1 reveal distinct atmospheric characteristics in terms of temperature and humidity between flights that produced persistent contrails and those that did not. This section aims to examine in greater detail how thermodynamic parameters evolve during flight, with a specific focus on C1. Fig. 7 shows the temporal evolution of these parameters, obtained by interpolating atmospheric data in space and time along the aircraft's trajectory.

The position of the aircraft, a twin-engine Boeing 777-224, was obtained using the ADS-B system. During the period of contrail formation, from 04:16 UTC to 04:20 UTC, relative humidity initially at 106 % increases to a peak at 118 % before decreasing to 111 %. This decrease in RH appears to be associated with a rise in temperature exceeding 218 K, while the specific humidity remains nearly constant within the range 0.057–0.058 g kg⁻¹. It is also associated with downward wind, and a slight reduction in horizontal wind speed. Similar variability in thermodynamic parameters is observed for other contrail cases.

## 5.3. Estimation of the spatial extension and optical properties of contrails

This section presents an estimation of the spatial extent of contrails. To assess their vertical extent, we use the scattering ratio of LIDAR signals to identify the contrail base and top. Fig. 8 presents the vertical profiles of the aerosol backscatter coefficient, scattering ratio, and volume and particle depolarization ratios between 4:34 and 4:44 UTC for C1, 5:16 to 5:24 UTC for C2, 5:28 to 5:35 UTC for C3, 5:42 to 5:45 UTC for C4 and 5:48 to 5:56 UTC for C5.

Contrail signatures on LIDAR profiles are in the altitude range between 9.7 and 10.1 km for C4 and between 10.3 and 10.8 km for the other four contrails. For C1, a main peak appears in the aerosol backscatter coefficient, which reaches the value of 0.004 km⁻¹ sr⁻¹ at the altitude 10.64 km. This peak is also visible in the scattering ratio, with the highest peak reaching SR = 15.85 (Fig. 8b). The maximum values of aerosol backscatter coefficient and scattering ratio are obtained for contrail C5 and are 0.49 km⁻¹ sr⁻¹ and 20.13 respectively at the altitude 10.67 km. These correspond to average values within the contrail layers of 0.021 km⁻¹ sr⁻¹ and 8.9, respectively. The signatures associated with the contrail C4 are at a lower altitude, between 9.7 and 10.1 km. The maximum values for volume linear depolarization ratio and particle linear depolarization are 0.13 and 0.17 respectively, corresponding to average values of 0.09 and 0.13. All values are summarized in Tables 2 and 3 (peak and mean values, respectively).

The contrail vertical extent is estimated by the difference between the top and the base altitudes of the contrail. The top altitude is estimated as the first altitude above the peak where the signal decreases below the mean of the 11–12 km scattering ratios (above the contrail). The uncertainty on this altitude is obtained as the first altitude above the peak where the signal decreases below the mean plus two standard deviations of the 11–12 km scattering ratios. The base altitude is obtained in the same manner, with the mean and standard deviation of scattering ratios calculated between 9 and 10 km (below the contrail).

The vertical extent of contrails C1 to C5 vary between 340 ± 10 and 440 ± 60 m as indicated in Table 4 which presents the spatial extent calculated for all contrails observed on June 2, 2023. The contrails are observed by the LIDAR between 15 min and 32 min after their formation. It is observed that older is the contrail, lower is its vertical extent. However, with only 5 cases observed the same morning, it is not enough to draw a conclusion.

The estimation of contrail width is reasonably consistent between the two methods, according to the uncertainties. Using the camera-based approach, the values range from 0.53 ± 0.10 km to 1.60 ± 0.44 km, while in the LIDAR-based method the values range from 0.35 km ± 0.14 to 1.90 ± 0.32 km.

Contrail C1 presents the largest width according to LIDAR
<figure><img src="image_9.png" /><figcaption>Fig. 7. ECMWF-ERA5 atmospheric parameters interpolated along the trajectory of the aircraft which has generated C1. The green vertical line marks the moment when the aircraft first appears on camera (04:16 UTC), while the red vertical line indicates when it exits the camera image (04:20 UTC). This time interval is considered as the contrail formation phase. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

observations (1.90 ± 0.32 km), whereas contrail C2 is the widest considering the camera estimation (1.60 ± 0.44 km). C4 is the least wide contrail for both estimations, although there is a notable discrepancy between the two methods (0.35 ± 0.14 km with LIDAR versus 0.53 ± 0.10 km with the camera).

One might expect that a longer time interval between the aircraft's passage and the observation would result in increasing the contrail width. This the case of C2, which has both the largest width estimated with the camera and the longest time elapsed between aircraft passage and observation. Conversely, C4, the narrowest contrail, corresponds to the shortest time interval. However, for the remaining contrails, no clear relationship is observed between the elapsed time and contrail width.

Poellot et al. (1999) have estimated horizontal extents ranging from 100 to 300 m for contrails less than 15 min old, while Freudenthaler et al. (1995) reported values between 1 km and 3 km for older contrails, up to 30 min old. In this study, the estimated contrail widths are in the same order of magnitude, lower than 2 km for contrails younger than 32 min, which is consistent with results of these two publications.

## 5.4. Determination of threshold values for the formation and persistence of contrails

According to Heymsfield et al. (2010), contrails can form when aircraft exhaust gases interact with ambient air exceeding the super-saturation threshold relative to liquid water. However, the environment needs to be supersaturated with respect to ice in order for contrails to persist (Iwabuchi et al., 2012).

To precisely characterize the formation and persistence thresholds of contrails on June 2, 2023, Fig. 9 presents the probability density functions of relative humidity and air temperature during the contrail formation period observed. These parameters are obtained through interpolation in latitude, longitude, altitude, and time at aircraft positions using ECMWF-ERA5.

Fig. 9 reveals three distinct groups of contrails:

* Short-lived contrails (lifetime <1 min) form in an environment with RHi between 45 % and 65 % (Fig. 9a) and temperatures between 210 K and 214.5 K (Fig. 9b, C6 to C7, C11).

* Moderately-lived contrails (lifetime <3 min) are associated with RHi values between 85 % and 100 % (Fig. 9a) and temperatures between 215 K and 216.5 K (Fig. 9b, C8 to C10). C12 is also a moderately-lived contrail within the same RHi range, but it formed at a higher temperature (218 K).

* Persistent contrails (C1 to C5), visible for more than two hours before exiting the camera field, form with RHi between 105 % and 120 % (Fig. 9a) and temperatures between 217 K and 218 K, occasionally reaching 223 K (Fig. 9b).

These observations are consistent with those of Low et al. (2025) who, based on 14 h of ground-based camera observations of 281 aircraft flights in London, reported that 73.3 % of the observed contrails were short-lived (< 2 min) and formed mainly under ice-undersaturated conditions (mean RHi of 81 %), 12.5 % had a lifetime between 2 and 10 min (with 59 % of these formed under conditions under saturation relative to ice) and 14.2 % were persistent (> 10 min), with 75 % formed under supersaturated conditions (mean RHi of 124 %). In addition, our results indicate that contrails can form even when RHi is below 100 %, while 105 % appears to be a critical threshold beyond which contrails persist for over two hours, as observed on June 2, 2023, over Clermont-Ferrand. Furthermore, persistent contrails tend to form in warmer temperature conditions. This suggests that, in this case study, water content and temperature could play a significant role in contrail formation and persistence.

## 6. Discussion and conclusion

In the present study, we have documented the formation and persistence of 12 contrails observed on June 2, 2023 over Clermont-Ferrand, using a complementary data set consisting of an all-sky camera, ADS-B recordings, LIDAR and ERA5 reanalysis data from ECMWF. The 12 contrails have been categorized into two groups: persistent (lasting more than 2 h) and non-persistent (lasting less than 3 min).
<figure><img src="image_10.png" /><figcaption>**Fig. 8.** LIDAR parameter profiles during observations of contrails C1 to C5. (a) Aerosol backscatter coefficient, (b) scattering ratio, (c) volume linear depolarization ratio and (d) particle linear depolarization. The colour code is the same as Fig. 3 (C1 grey, C2 blue, C3 red, C4 purple, C5 light orange). Statistical errors given by the SCC ELDA module are indicated by shaded areas. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

**Table 2**

Maximum values of LIDAR parameters (Profiles given in Fig. 8) associated with contrails C1 to C5 on June 2, 2023.

<table><thead><tr><th>Contrail</th><th>Backscattered signal (km<sup>-1</sup> sr<sup>-1</sup>)</th><th>Scattering ratio</th><th>volume depolarization</th><th>particle depolarization</th></tr></thead><tbody><tr><td>C1</td><td>0.038 ± 0.002</td><td>15.8 ± 0.8</td><td>0.20 ± 0.01</td><td>0.41 ± 0.08</td></tr><tr><td>C2</td><td>0.019 ± 0.001</td><td>8.3 ± 0.4</td><td>0.24 ± 0.01</td><td>0.40 ± 0.09</td></tr><tr><td>C3</td><td>0.033 ± 0.002</td><td>13.6 ± 0.7</td><td>0.23 ± 0.01</td><td>0.45 ± 0.01</td></tr><tr><td>C4</td><td>0.021 ± 0.001</td><td>8.4 ± 0.4</td><td>0.13 ± 0.01</td><td>0.17 ± 0.02</td></tr><tr><td>C5</td><td>0.049 ± 0.003</td><td>20.1 ± 1.2</td><td>0.15 ± 0.01</td><td>0.32 ± 0.12</td></tr></tbody></table>

**Table 3**

Mean values of LIDAR parameters (Profiles given in Fig. 8) in layers associated with contrails C1 to C5 on June 2, 2023.

<table><thead><tr><th>Contrail</th><th>Backscattered signal (km<sup>-1</sup> sr<sup>-1</sup>)</th><th>Scattering ratio</th><th>volume depolarization</th><th>particle depolarization</th></tr></thead><tbody><tr><td>C1</td><td>0.013 ± 0.001</td><td>6.1 ± 0.4</td><td>0.20 ± 0.02</td><td>0.23 ± 0.03</td></tr><tr><td>C2</td><td>0.011 ± 0.001</td><td>5.1 ± 0.4</td><td>0.18 ± 0.01</td><td>0.25 ± 0.03</td></tr><tr><td>C3</td><td>0.013 ± 0.001</td><td>6.0 ± 0.5</td><td>0.16 ± 0.02</td><td>0.23 ± 0.04</td></tr><tr><td>C4</td><td>0.011 ± 0.001</td><td>4.9 ± 0.4</td><td>0.09 ± 0.01</td><td>0.13 ± 0.02</td></tr><tr><td>C5</td><td>0.021 ± 0.002</td><td>8.9 ± 0.7</td><td>0.12 ± 0.01</td><td>0.15 ± 0.05</td></tr></tbody></table>

**Table 4**

Spatial extent estimation of persistent contrails observed on June 2, 2023. The time between the aircraft passage in the camera field and the LIDAR signature was estimated by taking the midpoint of the observation time intervals given in Table 1.

<table><thead><tr><th>Contrail</th><th>Time between aircraft passage and observation of the contrail at zenith (min)</th><th>Contrail width calculated from camera (km)</th><th>Contrail width calculated from LIDAR (km)</th><th>vertical extent (m) observed by LIDAR</th></tr></thead><tbody><tr><td>C1</td><td>20 ± 2</td><td>1.40 ± 0.60</td><td>1.90 ± 0.32</td><td>390 ± 60</td></tr><tr><td>C2</td><td>32 ± 2</td><td>1.60 ± 0.44</td><td>1.56 ± 0.29</td><td>340 ± 10</td></tr><tr><td>C3</td><td>28 ± 2</td><td>1.10 ± 0.33</td><td>0.94 ± 0.22</td><td>370 ± 20</td></tr><tr><td>C4</td><td>15 ± 2</td><td>0.53 ± 0.10</td><td>0.35 ± 0.14</td><td>440 ± 20</td></tr><tr><td>C5</td><td>20 ± 2</td><td>1.30 ± 0.33</td><td>1.08 ± 0.25</td><td>400 ± 60</td></tr></tbody></table>

Persistent contrails (C1 to C5) are associated with twin-engine aircraft, primarily Boeing 777s flying at 10,36 km or below. They are formed in air masses with RHI values between 107 and 120 % and temperatures ranging from 217 to 223 K. In addition, the wind was low with a horizontal wind speed around 5 to 7 m s⁻¹ and an upward vertical wind lower than 0.1 Pa s⁻¹. The optical properties of these contrails measured by LIDAR at 355 nm (unfortunately, the 532 and 1064 nm signals were not usable) indicate aerosol backscatter coefficients between 0.02 and 0.05 km⁻¹ sr⁻¹, scattering ratios between 8 and 20, volume linear depolarization between 0.13 and 0.24 and particle linear depolarization between 0.17 and 0.45. The vertical extents of these
<figure><img src="image_11.png" /><figcaption>**Fig. 9.** Relative frequency of relative humidity (a) and temperature (b) values along the aircraft path during (4 min on average) the observation period. The blue frame indicates contrails persisting for more than 2 h before leaving the camera's field of view, the green frame indicates contrails persisting for less than 3 min before disappearing, and the red frame indicates contrails persisting for less than 1 min before disappearing. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

contrails have been estimated by LIDAR from 340 ± 10 to 440 ± 20 m between 15 and 32 min after the aircraft's passage. Their horizontal extensions have been estimated with two independent methods, one based on LIDAR observation duration and wind speed, and another based on all sky camera image analysis. They vary from 0.53 ± 0.10 km and 1.60 ± 0.44 km and from 0.35 ± 0.14 km to 1.90 ± 0.32 km with the respective methods. These observations are consistent with previous airborne and satellite studies, supporting the representativeness of the case day. However, these estimates are based on observations made a few tens of minutes after the aircraft's passage and do not allow us to assess the evolution of the width of these contrails beyond approximately one hour of lifetime due to advection. The width of older contrails can also be measured using LIDAR, but it is more difficult to link them to the aircraft that produced them, and therefore to evaluate their width as a function of lifetime.

Conversely, non-persistent contrails (C6 to C12) are linked to both twin- and four-engine aircraft flying at higher altitude than 10,36 km, whereas persistent contrails were linked only to twin-engine aircraft. Very powerful twin-engine aircraft (such as the Boeing 777) can generate more contrails than lighter aircraft with more modern engines (such as those on Boeing 787 and Airbus A350), which consume less fuel and emit less soot, potentially reducing the formation of persistent contrails. However, contrail persistence may be more strongly influenced by engine operating conditions (e.g., thrust settings), fuel properties, and emission indices (water vapor and soot) than by engine type. Unfortunately, we cannot formally demonstrate this, as we do not have access to these technical details, as ADS-B data is our only source of information on the aircraft. Contrail persistence is also influenced by the surrounding atmospheric state: At this altitude, the air masses are characterized by less favourable atmospheric conditions for contrail formation, with RH values mostly below 100 %, and colder temperatures (from 214 K to 217 K) compared to persistent contrails (RH between 107 and 116 % and temperatures between 117 and 222 K).

To gain further insight into the microphysical properties of contrails, future studies could focus on a more detailed analysis of LIDAR signals to investigate the influence of wavelength on the depolarization and scattering of light, which was not feasible in the case study detailed in this article, where only a single LIDAR wavelength could be fully exploited. Such an approach would allow for a better characterization of particle size, shape, and their distribution along the plume, thereby enhancing our understanding of the microphysical processes involved

and their relationship to atmospheric conditions and aircraft characteristics responsible for contrail formation. In addition, further processing of the all-sky camera images could improve estimates of the spatial extent of contrails.

Recent advances in tools based on predictive artificial intelligence, coupled with observations by hemispherical (Gourgue et al., 2025) or satellite (Yu et al., 2024) cameras open the way for automated contrail classification and lifetime tracking using machine learning methods.

Finally, this study was based on a single day which may not be representative of broader meteorological conditions. Extending the investigation over a longer period with more observations would allow for broader climatological conclusions. Such an extended study would significantly enrich our understanding of microphysical processes and their link with atmospheric conditions and aircraft characteristics responsible for the observed contrails. It is also important to indicate that our results are largely based on ECMWF-ERA5 meteorological analyses. While the European analyses correspond to the state of the art about the physical atmospheric descriptions in coupling numerical modelling and data assimilation, water vapor in the upper air remains critical challenges. In a future step, collocated water vapor LIDAR observations will also be deployed for improving our knowledge of the water vapor spatial and temporal variability.

## Author contribution

PF, FP, PC and WB performed and analysed the measurements. All authors discussed the data and findings. SD, JLB, NM and PK prepared the manuscript with contributions from all co-authors.

## Financial support

The project BECOM has received funding from the Horizon Europe research and innovation actions program under grant agreement 101056885. CO-PDD is an instrumented site of OPGC observatory and LaMP laboratory, supported by Université Clermont Auvergne (UCA), Centre National de la Recherche Scientifique (CNRS-INSU) and Centre National d'Etudes Spatiales (CNES).
# Declaration of generative AI and AI-assisted technologies in the writing process

During the preparation of this work the authors used Google Translate and ChatGPT in order to improve the readability and language of the manuscript. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

## CRediT authorship contribution statement

**Sidy Diarra:** Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Jean-Luc Baray:** Writing – review & editing, Writing – original draft, Validation, Supervision, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Nadège Montoux:** Writing – review & editing, Validation, Supervision, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Patrick Fréville:** Writing – review & editing, Software, Resources, Methodology, Data curation. **Frédéric Peyrin:** Writing – review & editing, Software, Resources, Methodology, Data curation. **Philippe Cacault:** Writing – review & editing, Resources, Data curation. **William Boucher:** Writing – review & editing, Software, Resources, Methodology, Data curation. **Philippe Keckhut:** Writing – review & editing, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization.

## Declaration of competing interest

The authors declare that they have no conflict of interest.

## Acknowledgements

We acknowledge EARLINET and ACTRIS aerosol remote sensing teams for the scientific support, thanks to the use of the Single Calculus Chain that was essential for this study. The ERA5 reanalysis data used in this study were provided by ECMWF (https://doi.org/10.24381/cds.abdb2d47 and https://doi.org/10.24381/cds.bd0915c6) and generated using Copernicus Climate Change Service information (C3S). We acknowledge Emmanuel Delage for creating the DOI link to the dataset used in this study.

## Data availability

The datasets analysed in this study are available under DOI https://doi.org/10.25519/6Q9C-JV42.

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