# Tropical Cirrus Lifetime Estimated From Superpressure Balloon-Borne Lidar Observations

Thomas Lesigne, Aurélien Podglajen, François Ravetta

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Thomas Lesigne, Aurélien Podglajen, François Ravetta. Tropical Cirrus Lifetime Estimated From Superpressure Balloon-Borne Lidar Observations. Geophysical Research Letters, 2025, 52 (17), pp.e2025GL117353. 10.1029/2025GL117353. hal-05247878

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<figure><img src="image_1.png" /></figure>

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# Geophysical Research Letters®

## RESEARCH LETTER

10.1029/2025GL117353

**Key Points:**

* Half of the cirrus have a lifetime shorter than 1 hr, the mean lifetime is 6 hr

* The few longest-lived cirrus (12 hr or more) are responsible for most of the cloud coverage

* The results help constrain the role of various processes in the cloud life cycle

**Correspondence to:**

T. Lesigne,
thomas.lesigne@latmos.ipsl.fr

**Citation:**

Lesigne, T., Podglajen, A., & Ravetta, F. (2025). Tropical cirrus lifetime estimated from superpressure balloon-borne lidar observations. *Geophysical Research Letters*, *52*, e2025GL117353. https://doi.org/10.1029/2025GL117353

Received 2 JUN 2025
Accepted 21 AUG 2025

## Tropical Cirrus Lifetime Estimated From Superpressure Balloon-Borne Lidar Observations

<figure><img src="image_2.png" /></figure>

Thomas Lesigne¹, Aurélien Podglajen², and François Ravetta¹

¹LATMOS/IPSL, Sorbonne Université, UVSQ, CNRS, Paris, France, ²LMD/IPSL, École Polytechnique - Institut Polytechnique de Paris, Sorbonne Université, École Normale Supérieure - PSL Research University, CNRS, Paris, France

**Abstract** Tropical tropopause layer (TTL) cirrus clouds play a key role in the Earth climate system. Yet the relative role of the various processes shaping them remains poorly known. Characterizing the temporal evolution of cloudy structures from observations is essential to address this issue but represents a challenge. Indeed, space- and airborne platforms move fast and mainly provide instantaneous snapshots. In boreal winter 2021–2022, two balloon-borne lidars flew over the Equatorial Pacific Ocean, slowly drifting above the clouds. We use those unique nighttime observations to quantify the distribution of TTL cloud lifetime above this homogeneous region. This distribution is strongly asymmetric: half of the clouds live less than 1 hr, but their mean lifetime is about 6 hr. The few long-lived clouds (>12 hr) dominate the cloud cover. Those results compare reasonably well with TTL cirrus lifetimes in the ERA5 reanalysis, although the modeled TTL cloud cover is largely underestimated.

**Plain Language Summary** High and thin tropical ice clouds have a significant impact on the climate. They are affected by numerous processes acting on different timescales. A better understanding of these processes and their timescales requires observations of clouds' lifetime. Satellites and planes fly too fast to capture the temporal evolution of cloudy structures. Stratospheric balloons, slowly drifting above the clouds, overcome this limitation. Such balloon-borne observations have been performed over the Pacific Ocean during the 2021–2022 boreal winter. From the nighttime-only observations we reconstruct the underlying distribution of cloud lifetime. The median lifetime is 1 hr and the mean lifetime 6 hr. Long-lived clouds (lifetime longer than 12 hr) dominate the cloud cover. Tropical cirrus lifetimes in the European meteorological reanalysis ERA5 are in good agreement with our findings, but the model lacks a significant amount of cirrus clouds.

## 1. Introduction

Tropical tropopause layer (TTL) cirrus impact the Earth's climate, modulating its radiative budget both directly (Fueglistaler et al., 2009; Hartmann et al., 2001), and indirectly through the regulation of the lower stratosphere water vapor amount (Holton et al., 1995; Riese et al., 2012; Solomon et al., 2010). Ubiquitous vertically thin cirrus around the tropical cold point tropopause have been observed from ground-based (e.g., Boehm & Verlinde, 2000; Mather et al., 1998; Uthe & Russell, 1977), ship-based (Fujiwara et al., 2009), airborne (e.g., Jensen et al., 2017; Peter et al., 2003), and space-borne lidars (e.g., Taylor et al., 2011; Winker & Trepte, 1998).

The persistence of these clouds and their uncertain longevity have led to controversy regarding the dynamical, radiative or microphysical processes shaping them. Several studies have highlighted the ubiquitous influence of waves on TTL cirrus (Chang & L'Ecuyer, 2020; Jensen et al., 2013; Kim et al., 2016). From a dynamical point of view, the TTL wave spectrum features a wide range of time scales, ranging from several days to a few minutes (Podglajen, Hertzog, et al., 2016). Empirically, TTL cirrus occurrence is highly correlated with specific phases of low-frequency tropical waves (Boehm & Verlinde, 2000; Bramberger et al., 2022; Kim et al., 2016). However, microphysical processes such as ice nucleation also strongly respond to high-frequency gravity waves (Corcos et al., 2023; Dinh et al., 2016; Jensen et al., 2013). Hence, it is unclear which waves most affect cirrus life cycle.

Similarly, the relative role of (wave-induced) synoptic uplift (Luo et al., 2003; Podglajen et al., 2018) and radiatively driven cloud-scale circulation in maintaining TTL cirrus layers against ice sedimentation remains a matter of debate (Boehm et al., 1999; Dinh & Fueglistaler, 2014; Dinh et al., 2010, 2012, 2014; Jensen et al., 2011; Podglajen, Plougonven, et al., 2016). Here again, a reliable estimate of cirrus lifetime would help address this issues, since radiatively driven cloud-scale circulations requires 12–24 hr to build up (Jensen et al., 2011). Finally,

<footnote>© 2025. The Author(s).
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</footnote>
in terms of impacts, for the same coverage, the degree to which cirrus clouds dehydrate their environment also depends on their lifetime (Fueglistaler & Baker, 2006).

While TTL cirrus lifetimes have been reported in the literature, lying between a few hours to a few days (Fujiwara et al., 2009; Peter et al., 2003; Taylor et al., 2011; Uthe & Russell, 1977; Winker & Trepte, 1998), these estimates remain crude and statistics are lacking. Ground- and ship-based lidars are intermittently blinded by opaque clouds below the TTL. Space- and airborne observations do not suffer from this limitation, but provide quasi-instantaneous snapshots, making it difficult to infer the actual cloud lifetime. Winker and Trepte (1998) and Taylor et al. (2011) had to assume clouds' continuity between consecutive satellite overpasses over the same region to suggest that TTL cirrus could persist up to a few days.

During boreal winter 2021–2022, in the frame of Strateole-2 project (Bramberger et al., 2022, 2023; Cao et al., 2022; Corcos et al., 2021; Haase, 2018; Kalnajs et al., 2021), several cloud profiling microlidars were flown onboard long-duration stratospheric balloons (Ravetta et al., 2020). Slowly drifting with the flow, these instruments gathered unprecedented observations to follow the evolution of cloudy structures at high temporal resolution (Lesigne et al., 2024).

In this paper, we investigate TTL cirrus lifetime distribution over the Equatorial Pacific Ocean from two balloon flights during the 2021–2022 boreal winter. In Section 2 we present a fitting method to assess cirrus lifetime distribution from truncated observed distributions. The fitted distribution is presented and discussed in Section 3, and those results are finally used to assess TTL cirrus lifetimes in the ERA5 reanalysis (Hersbach et al., 2020).

## 2. Methodology

### 2.1. Data Set

The Balloon-borne Cirrus and convective overshOot Lidar (BeCOOL) has been designed to operate in the extreme conditions of the lower tropical stratosphere during Strateole-2 campaigns (Ravetta et al., 2020). It is a light-weight (~7 kg) low-power (~10 W) elastic backscatter nadir-pointing lidar, derived from the Ice-Atmosphere-Ocean-Observing System microlidar (Mariage et al., 2017). BeCOOL operates at a wavelength of 802 nm, its native vertical sampling is 15 m and temporal resolution 1 min. This instrument provides nighttime measurements, with an operating cycle of 10 min ON / 10 min OFF.

During Strateole-2 first scientific campaign, between November 2021 and January 2022, two BeCOOL lidars sampled the Equatorial Pacific Ocean, the balloons going back and forth over the warm pool as the Quasi-Biennial Oscillation was switching phases (Figure 1a). Eighty nights of observations have been gathered East of 159°E.

We use the BeCOOL attenuated backscatter data set (normalized lidar return) and define the Scattering Ratio (SR) as the ratio of the attenuated backscatter to the expected clear sky one. As we focus on the TTL, these observations are restricted to altitudes above 14 km. The temporal resolution is coarsened to 20 min, averaging the ten 1-min profiles of each operating cycle (10 min ON / 10 min OFF) and assuming it to be representative of the full cycle. The vertical resolution of the observations is coarsened to 250 m to mitigate the effect of thermal-induced instrumental noise, which systematically decreases along the night as the instrument cools down. The effective duration of nocturnal observation windows ranges from 8 to 12 hr, with a median duration of 11 hr.

### 2.2. Definitions

In the 2-dimensional (time × altitude) lidar observations, hereafter referred to as “lidar curtain,” we define a cloud as a continuous patch of enhanced SR. A threshold of 1.5 is set to distinguish clouds (SR ≥ 1.5) from clear sky (SR < 1.5). Two pixels are considered contiguous if they share one of their four edges (4-connected). The SR threshold value was chosen to take advantage of BeCOOL’s unique sensitivity to very thin clouds. We verified that any threshold within the 1.25–1.75 range yields similar results, whereas higher thresholds tend to split single clouds into shorter, disconnected patches. In a similar fashion, in the 2-dimensional ERA5 curtains (time × altitude, either balloon-following or Eulerian), we define a cloud as a continuous patch of non-zero specific cloud ice water content (CIWC > 0 kg·kg⁻¹).

The apparent lifetime τ of a cloud is defined as the object's extension along the temporal dimension in the 2-dimensional curtain (time × altitude). Hereafter, the *lifetime* refers to the balloon-following apparent lifetime.
<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /><figcaption>**Figure 1.** (a) Strateole-2 balloons' tracks over the Pacific Ocean and (b) example of cloud observations and classification for two different nights: *fully resolved* clouds are in blue, *partially resolved* clouds in yellow, *night long* clouds in red; and (c) apparent lifetime distributions and partitioning in the three classes (same colors).</figcaption></figure>
The Eulerian lifetime, introduced in Section 3.2, refers to the apparent lifetime which would be observed from a fixed position (e.g., ground-based station, or a stationary balloon). Note that, due to shear between the balloons' and the clouds' levels, the balloon-following definition of the lifetime, at the scale of cloudy structures, differs from a Lagrangian definition following an air parcel (duration with ice present in the parcel).

Beyond the characterization of clouds as individual objects, a parameter that impacts large-scale radiative forcing is the areal and temporal cloud cover. As emphasized by Spänkuch et al. (2022, Table 2), different quantities related to the amount of clouds can be defined depending on the type of observations or models involved. Here, from the lidar point of view, we define the cloud fraction as the percentage of cloudy lidar profiles (at least one cloud detection within the profile). The relations between cloud cover estimates from different viewing geometries has been previously investigated (Berg & Stull, 2002; Kassianov et al., 2005). In our case, due to the extensive length of the data set and under the assumption of ergodicity, the cloud fraction previously defined is likely a reasonable proxy for the areal cloud cover. In order to estimate the contribution to this cloud fraction from clouds with a lifetime $\tau$ in the range $[a, b]$, we introduce the “Effective Coverage” $EC(a \le \tau \le b)$ defined as:

$$EC(a \le \tau \le b) = \frac{1}{\bar{\tau}} \int_{a}^{b} \tau f(\tau) d\tau \qquad (1)$$

where $f$ is the Probability Density Function (PDF) of cloud lifetimes and $\bar{\tau}$ its mean. Assuming that $f$ is known, $EC$ is also a conditional probability: if at a given instant a cloud is observed, $EC(a \le \tau \le b)$ is the probability for its lifetime to be in the range $[a, b]$.

## 2.3. Observed Apparent Lifetime

Within the 80 lidar curtains, clouds are detected following the previous definition. Each cloud is classified depending on the way it appears on the curtain: fully resolved if both temporal ends are detected within the night, partially resolved if only one end is detected, and night long if no ends are detected. A total of 201 clouds are detected, of which 97 are classified as fully resolved, 70 as partially resolved and 34 as night long.

Examples of cloud detection and classification are shown on Figure 1b, the partitioning in three classes is shown on Figure 1c, along with the partial lifetime distributions for the fully and partially resolved classes. In line with findings from Sassen et al. (2009), reporting no consistent diurnal cycle in TTL cirrus over the Equatorial Pacific Ocean, the local time distribution of detected clouds' ends do not show any clear pattern that would support the existence of such a cycle. The observed TTL cloud fraction is 71% (i.e., cloud free TTL account for 29% of the profiles). Regarding the vertical structure of clouds, a single cloud layer appears in 61% of the profiles, two overlapping cloud layers in 10%. Overlaps of more than two clouds in the TTL are negligible.

## 2.4. Fitting Lifetime Distribution

We aim at estimating the underlying continuous distribution of lifetimes (nighttime and daytime). This estimate should match the observed partitioning between the three classes and the partial distributions for the fully and partially resolved classes. To do so, we build synthetic observations from different assumed lifetime distributions and evaluate their goodness-of-fit to the observations using an ad hoc score. We chose to focus on classical analytical distributions (e.g., exponential, power-law, log-normal), but our method could also be applied to non-analytical distributions, for instance empirical ones.

In order to avoid unrealistically long-lived clouds, we set an upper bound $\tau_{max}$ for cirrus lifetime. Although it is impossible to estimate a maximum cirrus lifetime from our observations, we note that the longest segment with consecutive occurrence of night long cirrus (which could constitute a single long-lived cloud) is 5 days, and arbitrarily set the upper bound at twice this value: $\tau_{max} = 10$ days. The sensitivity of the retrieved lifetime distribution to $\tau_{max}$ is discussed in Section 3.1. If $\mathcal{F}$ is the PDF of a given distribution, the PDF $f$ of the respective truncated distribution ($0 < \tau < \tau_{max}$) scales as:

$$f(\tau) = \frac{\mathcal{F}(\tau)}{K} \quad \text{where} \quad K = \int_{0}^{\tau_{\max}} \mathcal{F}(\tau') d\tau' \qquad (2)$$
From an assumed distribution, 1,000 lifetime values are randomly picked (removing values larger than $τ_{max}$). Each of these lifetimes is associated with a birth time equiprobably picked over [0, 24 h]. Then a nighttime-only mask is applied on those synthetic clouds to mimic the diurnal observational cycle of the balloon-borne lidar (effective observation window of 11 over 24 hr). The remaining clouds are finally classified the same way as the observations.

The agreement between real and synthetic partitioning in the three classes is evaluated with the root mean square error $ε$ on the fraction of clouds $X$ in each class:

$$ε = \sqrt{\sum_{class} (X_{obs} - X_{synth})^2} \qquad (3)$$

For the fully and partially resolved classes, the agreement between observed and synthetic distributions are evaluated on hourly binned data from permutation tests (Good, 2005) using Kolmogorov-Smirnov statistic (Gibbons & Chakraborti, 2003). These tests are performed with 2,000 permutations. P-values for both the fully and partially resolved classes, $p_{full}$ and $p_{partial}$, are used to assess the hypothesis that the observed and synthetic lifetimes originate from the same underlying distribution: the greater those p-values, the more similar the observed and synthetic distributions. Finally, the best fit is found maximizing a comprehensive score $S$ defined as:

$$S = (p_{full} \cdot p_{partial})/ε \qquad (4)$$

This score $S$ inherits random behaviors from both the random sampling and permutations (two realizations of the fit for a given distribution will throw two different scores). Hence, to find the best distribution, we average the score $S$ over 5,000 samples of 1,000 random lifetimes each.

# 3. Results and Discussion

## 3.1. Lifetime Distribution

Several classical analytical distributions have been tested as candidates for the underlying apparent lifetime distribution. We evaluated these distributions using the score $S$ over extensive sets of parameters. The best scores were achieved using log-normal distributions, defined by two parameters ($σ$, $τ_μ$), where $τ_μ$ is the geometric mean, which has the same dimension as the variable (here the time), and $σ$ the geometric standard deviation, which is dimensionless. For convenience, we used $ln(σ)$ instead of $σ$ as a parameter. We explored this parameter space and, for $τ$ in hours, the best fit has been realized with a truncated log-normal law of parameters $ln(σ) = 2.05$ and $τ_μ = 0.95$ hr. After normalization of the log-normal law $F$ following Equation 2, the truncated log-normal PDF $f$ is given by:

$$f(τ) = \frac{1}{K \ln(σ) τ \sqrt{2π}} \exp\left(-\frac{[ln(τ/τ_μ)]^2}{2[ln(σ)]^2}\right) \quad \text{if } 0 < τ < τ_{max}, \quad f(τ) = 0 \text{ otherwise} \qquad (5)$$

The mean lifetime $τ̄$ is 5.8 hr and the median 0.95 hr. The score $S$, averaged over 5,000 realizations for each set of ($ln(σ)$, $τ_μ$), is displayed on Figure 2. The patch of enhanced score around the peak score $S_{max}$ appears to lay along the iso mean-lifetimes. Using $S_{max}/2$ as a threshold value around $S_{max}$, the mean lifetime $τ̄$ remains in the range 5.8 ± 0.9 hr, which is well within our ~11 hr observation window.

Figure 3a shows the partial lifetime distributions of observed and synthetic truncated lifetimes (nighttime only) derived from an extensive sample of 1.000.000 synthetic lifetimes picked from the fitted distribution. Besides the partitioning in the three classes, which is well reproduced (differences below 2%), synthetic and observed distributions appear in fair agreement for both the fully and partially resolved classes. The PDF of the fitted distribution is displayed on Figure 3b. Note that the fit mostly constrains the 1–11 hr range in which observations are directly available, the tails of the distribution are poorly constrained. The main relevant features of this fitted distribution are its decreasing rate within the constrained range and the overall weight of its long tail, which account for the relative frequency of the partially resolved and night-long clouds. The peak of the log-normal
<figure><img src="image_6.png" /><figcaption>**Figure 2.** Score *S* averaged over 5,000 realizations for each set of the truncated log-normal law parameters (ln(*σ*), τμ), iso mean-lifetimes drawn as dashed lines. The red cross corresponds to the peak score Smax.</figcaption></figure>

distribution, located at approximately 1.5 min, lies well outside of the con-
strained range and should not be considered a reliable feature.

Most cirrus appear to be very short-lived, but how much do they actually
contribute to the cloud cover? The percentages of clouds of different lifetime
ranges and their Effective Coverage (as defined in Section 2.2) are summa-
rized in Table 1. The cloud cover appears to be dominated by the few long-
lived clouds (τ > 12 hr, too long to be fully observed with the balloon-
borne lidar), whereas the numerous short-lived clouds contribute very little
to the total coverage.

Finally, we investigated the sensitivity of our estimates to the chosen lifetime
upper bound τmax. Repeating the whole fitting procedure for τmax values in the
range 5–20 days, the estimated mean lifetime τ̄ changes by less than 0.2 hr, the
Effective Coverage values reported in Table 1 by less than 1%. Hence, the
exact value of the upper bound τmax has limited impact on our results and the
mean lifetime τ̄ appears to be a robust outcome of this study.

3.2. Assessing TTL Cirrus Lifetime in the ERA5 Reanalysis

To illustrate how the previous results can be used for model evaluation, we
compare them to TTL cirrus lifetimes in the ERA5 reanalysis. Specific CIWC
from ERA5 (hourly data, model levels, 0.25° latitude × 0.25° longitude
resolution) has been interpolated on a fixed altitude grid (median altitudes of

ERA5 model levels) and along the balloons' tracks. Clouds are detected following the definition given in Sec-
tion 2.2. First, we compare the full day-and-nighttime apparent lifetime distribution from the model to our fitted
distribution, then, masking daytime data, we compare the truncated nighttime-only apparent lifetime distribution
from the model to the observed apparent lifetime. Both comparisons are displayed on Figure 4.

The distribution of full lifetimes from ERA5 compares fairly well with the fitted lifetime distribution, albeit cirrus living less than 12 hr appear relatively less frequently in the model. Despite this discrepancy, the mean lifetimes are in fair agreement: 5.8 hr for the fitted distribution and 6.5 hr in the model. Distributions of nighttime-only truncated lifetimes are also in fairly good agreement. Shorter-lived clouds are relatively more frequent in the observations, which could be partly attributed to the different temporal resolutions (20 min for the observations, 1 hr for the model).

Regarding the cloud cover, the nighttime-only TTL cloud fraction (as defined in Section 2.2) in the reanalysis
along the balloons' tracks is only 33% (32% for the day-and-nighttime) versus 71% in the lidar observations.

<figure><img src="image_7.png" /><figcaption>**Figure 3.** (a) Apparent lifetime distributions for the synthetic and observed lifetimes (nighttime only), and partitioning in the three classes and (b) probability density function of the fitted lifetime distribution.</figcaption></figure>
<caption>**Table 1**</caption>

Percentage of Clouds and Effective Coverage for Different Lifetime Ranges ($\tau_{max}$ = 10 d)

<table><thead><tr><th>Lifetime range</th><th>τ < 1 hr (%)</th><th>1 hr < τ < 6 hr (%)</th><th>6 hr < τ < 12 hr (%)</th><th>12 hr < τ (%)</th></tr></thead><tbody><tr><td>Percentage of clouds</td><td>50</td><td>32</td><td>8</td><td>10</td></tr><tr><td>Effective coverage</td><td>3</td><td>15</td><td>12</td><td>71</td></tr></tbody></table>

Despite the fair agreement for TTL cloud mean lifetime, the cloud cover in the reanalysis appears to be significantly underestimated, more than half of the total coverage is missing.

Using the ERA5 reanalysis, we can also investigate the difference between Eulerian observations and balloon- following observations. To address this point, we extracted Eulerian curtains over each of the 1,258 model grid point under the balloon's tracks and over the same period (November 2021–January 2022). The distributions of Eulerian lifetime (as defined in Section 2.2) are displayed in light blue on Figure 4a. The spread of the light blue beam clearly illustrates the variability of these Eulerian lifetime distributions. The balloon-following distribution is well contained within this Eulerian beam. Moreover the thumbnail plot in Figure 4a shows that its 6.5 hr mean is aligned with the mode of Eulerian mean lifetime distribution. Thus balloon-following statistics do not seem inherently different from Eulerian ones, and previous results could easily be used as such to evaluate the lifetime of TTL cirrus in simulations.

## 4. Conclusion

In the present study, TTL cirrus lifetimes were estimated for the first time using balloon-borne lidar observations over the Equatorial Pacific Ocean in boreal winter 2021–2022. As the lidar only operates at night, observations are limited to ~11 hr windows. Assuming no diurnal cycle in TTL cirrus statistics, we developed a fitting method to estimate the underlying lifetime distribution (nighttime and daytime) of those clouds.

TTL cirrus lifetime distribution is strongly skewed, with a median value about 1 hr and a mean about 6 hr (5.8 ± 0.9 hr). Although short-lived clouds are dominating this distribution, their contribution to the cloud cover remains small. About 70% of the coverage is from the 10% of clouds with a lifetime of 12 hr or more. Coming back to dynamical processes, this variety of lifetimes is comparable to the wide range of time scales of TTL gravity waves. However, the clouds dominating the coverage generally match the duration of the cold phase of low-frequency waves, suggesting that high-frequency gravity waves are secondary in setting the lifetime. These cirrus may also last long enough for cloud radiation-dynamics feedbacks to develop (Jensen et al., 2011). Thus, the latter processes may not be a priori overlooked when simulating TTL cirrus clouds.

<figure><img src="image_8.png" /><figcaption>**Figure 4.** (a) Distributions of ERA5 apparent cirrus lifetimes (along the balloons' tracks), ERA5 Eulerian cirrus lifetimes (over each model grid point overflown by the balloons) and Balloon-borne Cirrus and convective overshOOt Lidar (BeCOOL) observation-fitted apparent cirrus lifetimes, ERA5 mean lifetime distribution in thumbnail plot; (b) distributions of nighttime-only apparent truncated lifetimes from ERA5 and from BeCOOL lidar observations.</figcaption></figure>
The deduced TTL cirrus lifetime are similar in the ERA5 reanalysis along the balloon's tracks, although the TTL cloud cover appears largely underestimated in the reanalysis. Assessing TTL cirrus in ERA5 would require a dedicated study, but this general agreement in terms of lifetime suggests that at least some of the key processes (e.g., low-frequency equatorial waves) are reasonable well represented in the reanalysis. Using ERA5 data, we showed that balloon-following quantities do not inherently differ from Eulerian ones. Hence, within the validity range of this study (Equatorial Pacific Ocean, 2021–2022 boreal winter), our results could be considered similar to Eulerian ones and directly used to evaluate TTL cirrus lifetimes in model simulations.

Future instrumental developments of the BeCOOL microlidar are planned to enable daytime measurements, which will help overcome our current assumptions regarding diurnal variability. Meanwhile, further characterization of these clouds is underway. In particular, we plan to estimate their radiative impact and contribution to the TTL water vapor budget, as well as the processes controlling their life cycle. These investigations require detailed analysis of the cloud microphysical properties and are performed using a combination of observations and Lagrangian microphysical simulations.

## Data Availability Statement

The Strateole-2 BeCOOL data set (LATMOS/IPSL, 2024) is available from the IPSL Data Catalog (https://doi.org/10.14768/bad47567-f844-4084-abd9-917668e18d82, LATMOS/IPSL, 2024). ERA5 data set (ECMWF, 2020) is available through Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.143582cf.

## Acknowledgments

The authors are grateful to Eric Jensen for encouragements and helpful comments during the early phase of this study. The BeCOOL data set was collected as part of Strateole-2, which was sponsored by CNES, CNRS/INSU, and NSF. Thomas Lesigne acknowledges support by a doctoral grant from CNES. This study has been supported by the Agence Nationale de la Recherche (Grant ANR-21-CE01-0016) and benefited from the IPSL Data and Computing Center ESPRI which is supported by CNRS, SU, CNES, and Ecole Polytechnique.

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