# Atmospheric Turbulence in the Upper Troposphere and Lower Stratosphere From Airborne Observations During the DCOTSS Field Campaign

Soo-hyun Kim, Rei Ueyama, Rachel Atlas, Jonathan Dean-day, Paul Bui, Jessica B Smith, Aurélien Podglajen, Cameron R Homeyer

► To cite this version:

Soo-hyun Kim, Rei Ueyama, Rachel Atlas, Jonathan Dean-day, Paul Bui, et al. Atmospheric Turbulence in the Upper Troposphere and Lower Stratosphere From Airborne Observations During the DCOTSS Field Campaign. Journal of Geophysical Research: Atmospheres, 2025, 130 (21), pp.e2025JD044556. 10.1029/2025jd044556. hal-05383959

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Distributed under a Creative Commons Attribution 4.0 International License
# JGR Atmospheres

## RESEARCH ARTICLE

10.1029/2025JD044556

**Key Points:**

* Strong turbulence occurs frequently within 2 km above the tropopause, especially in moist plumes near deep convection

* Observed turbulence is associated with large-scale flow disturbances near convection, including enhanced shear, deformation, and divergence

* Turbulence shows weak overall correlation with water vapor amount, but its impact depends on location and environmental conditions

**Supporting Information:**

Supporting Information may be found in the online version of this article.

**Correspondence to:**

S.-H. Kim,
soohyun.kim@nasa.gov;
soohyun.kim9012@gmail.com

**Citation:**

Kim, S.-H., Ueyama, R., Atlas, R., Dean-Day, J., Bui, P., Smith, J. B., et al. (2025). Atmospheric turbulence in the upper troposphere and lower stratosphere from airborne observations during the DCOTSS field campaign. *Journal of Geophysical Research: Atmospheres*, 130, e2025JD044556. https://doi.org/10.1029/2025JD044556

Received 8 JUN 2025

Accepted 9 OCT 2025

**Author Contributions:**

**Conceptualization:** Soo-Hyun Kim,
Rei Ueyama

**Formal analysis:** Soo-Hyun Kim,
Rei Ueyama

**Funding acquisition:** Soo-Hyun Kim

**Investigation:** Soo-Hyun Kim,
Rei Ueyama, Rachel Atlas, Jonathan Dean-
Day, Paul Bui, Jessica B. Smith,
Aurélien Podglajen, Cameron R. Homeyer

**Methodology:** Soo-Hyun Kim,
Rei Ueyama

© 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.

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

## Atmospheric Turbulence in the Upper Troposphere and Lower Stratosphere From Airborne Observations During the DCOTSS Field Campaign

Soo-Hyun Kim¹,² ID, Rei Ueyama² ID, Rachel Atlas³ ID, Jonathan Dean-Day²,⁴ ID, Paul Bui², Jessica B. Smith⁵ ID, Aurélien Podglajen³ ID, and Cameron R. Homeyer⁶ ID

¹NASA Postdoctoral Program Fellow, NASA Ames Research Center, Moffett Field, CA, USA, ²Earth Science Division, NASA Ames Research Center, Moffett Field, CA, USA, ³Laboratoire de Météorologie Dynamique, IPSL, CNRS, ENS-PSL/École Polytechnique/Sorbonne Université, Paris, France, ⁴Bay Area Environmental Research Institute, Moffett Field, CA, USA, ⁵Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA, ⁶School of Meteorology, University of Oklahoma, Norman, OK, USA

**Abstract** Turbulence in the upper troposphere and lower stratosphere (UTLS) plays an important role in total energy dissipation and in the transport and mixing of heat, momentum, and atmospheric constituents while also posing significant hazards to aviation. In this study, we leverage high-altitude in situ observations from the NASA Dynamics and Chemistry of the Summer Stratosphere (DCOTSS) field campaign—including data acquired near convection and at altitudes rarely covered by commercial aircraft—to investigate turbulence and its potential link to UTLS composition. We find that strong turbulence occurs frequently within 2 km above the tropopause, which corresponds to 1–2 km above deep convective cloud tops, particularly within moist convective plumes characterized by enhanced water vapor. The occurrence of strong turbulence increases with proximity to convection with elevated frequencies persisting out to 100 km from convective cores. In particular, enhanced turbulence potential occurs near UTLS convective outflows, which is supported by favorable environmental factors such as strong vertical wind shear, flow deformation, and large divergence tendencies. Furthermore, although turbulence intensity and water vapor are generally weakly correlated, positive correlations occur more frequently near the tropopause and under strong vertical wind shear, suggesting that turbulent mixing may contribute to localized stratospheric moistening under certain atmospheric conditions. The findings demonstrate the utility of high-altitude in situ observations for advancing our understanding of UTLS turbulence and its influence on atmospheric composition, with implications not only for climate but also for future high-altitude aviation applications.

**Plain Language Summary** Turbulence in the upper troposphere and lower stratosphere plays a key role in Earth's atmosphere and poses significant hazards to aviation. In this study, we leverage high-altitude in situ observations from a recent NASA airborne field campaign—including data sampled near thunderstorms and at altitudes rarely reached by commercial aircraft—to investigate turbulence and its potential link to greenhouse gases such as water vapor. We find that strong turbulence occurs frequently within 2 km above the tropopause, corresponding to about 2 km above cloud tops, particularly within moist air influenced by thunderstorms. The observed turbulence is associated with unstable environmental conditions potentially induced by thunderstorms. We also find that turbulence is weakly correlated with stratospheric water vapor, but its impact varies with location and background environmental conditions. These findings demonstrate the utility of high-altitude in situ observations for advancing our understanding of turbulence and its influence on greenhouse gases, with implications not only for climate but also for future high-altitude aviation applications.

## 1. Introduction

Atmospheric turbulence in the UTLS plays an important role in total energy dissipation in the atmosphere and contributes to the transport and mixing of heat, momentum, and atmospheric constituents (Cadet, 1977; Pavelin et al., 2002; Podglajen et al., 2017; Shapiro, 1980). In the aviation context, turbulence remains a leading cause of weather-related accidents, posing threats to flight safety and operational efficiency (National Transportation Safety Board, 2021; Sharman & Lane, 2016). In addition, turbulence may influence climate by inducing changes in the spatial distribution and local gradients of trace gases such as water vapor and ozone, which can lead to radiative perturbations that impact climate (Solomon et al., 2010). It may also lead to increased fuel consumption
**Validation:** Soo-Hyun Kim, Rei Ueyama, Rachel Atlas, Jonathan Dean-Day, Paul Bui, Jessica B. Smith, Aurélien Podglajen, Cameron R. Homeyer
**Writing – original draft:** Soo-Hyun Kim
**Writing – review & editing:** Soo-Hyun Kim, Rei Ueyama, Rachel Atlas, Jonathan Dean-Day, Paul Bui, Jessica B. Smith, Aurélien Podglajen, Cameron R. Homeyer

and emissions due to longer flight times associated with turbulence avoidance (Irvine et al., 2016; J.-H. Kim et al., 2020).

Among various sources of UTLS turbulence (e.g., jet-frontal systems, mountains, moist convection), moist convection is one of the major sources, referred to as convectively induced turbulence (CIT; Lane et al., 2012; Sharman & Lane, 2016). Convection modifies surrounding environmental conditions, for example, by enhancing local vertical wind shear and reducing static stability in the outflow region, thereby favoring turbulence generation (e.g., Gisinger et al., 2024; J.-H. Kim et al., 2014; Sharman & Trier, 2019; Trier & Sharman, 2009, 2016; Trier et al., 2012). In addition, vertically and horizontally propagating gravity waves excited by convection can perturb the background flow, thus lowering the local Richardson number, and can directly induce turbulence in their breaking regions (Fritts & Alexander, 2003; S.-H. Kim et al., 2019; J.-H. Kim & Chun, 2012; Lane et al., 2003; Trier et al., 2012; Sharman & Trier, 2019). Several recent observational studies have demonstrated the prevalence and spatial extent of turbulence near convection (e.g., Baek et al., 2024; Hitchcock et al., 2025; Honomichl et al., 2013; Lane et al., 2012). For example, a recent study by Hitchcock et al. (2025) utilized long-term (2009–2017) archived commercial turbulence reports and coincident weather radar data to characterize the spatial distribution of near cloud turbulence over the United States. Their results showed that elevated turbulence risk can extend horizontally well beyond 32 km from convection, and that the largest lateral extents typically occur for aircraft flying near the tropopause (up to 3 km below), especially for storms whose echo tops approach the tropopause. Because their analysis was based on turbulence reports from commercial aircraft, which operate below 15 km, turbulence near convection at higher altitudes was not examined.

Beyond its role in generating turbulence and rapid vertical transport, deep convection that penetrates the tropopause can inject water vapor and other constituents from the lower troposphere into the UTLS, thereby promoting irreversible mixing across the tropopause (i.e., stratosphere-troposphere exchange, STE) and altering atmospheric composition (Holton et al., 1995; Stohl et al., 2003). Turbulence associated with such convection may further enhance this mixing, particularly for water vapor because of its large vertical gradient in the UTLS. Understanding this relationship is particularly important, as UTLS water vapor results in tropospheric warming and lower stratospheric cooling (Forster & Shine, 1999; Solomon et al., 2010) and can activate ozone-destroying stratospheric chemistry (Anderson et al., 2012, 2017; Dvortsov & Solomon, 2001; Robrecht et al., 2021). Moreover, the projected increase in convective frequency and CIT in a warming climate (Del Genio et al., 2007; Diffenbaugh et al., 2013; Lepore et al., 2021; S.-H. Kim et al., 2023; Tinney et al., 2024) underscores the need for dedicated in situ observations of turbulence and UTLS composition. Despite its significance, the impact of turbulence on UTLS composition remains underexplored.

Over the last decade, a renewed effort has been undertaken to characterize turbulence occurrence using various observational platforms and systems, including aircraft, radiosondes, ground-based radar and lidar, and meteorological towers equipped with sonic anemometers (e.g., Atlas et al., 2025; Bodini et al., 2019; J. Kim et al., 2021; J.-H. Kim et al., 2021; S.-H. Kim et al., 2020, 2022; Ko et al., 2024; Muñoz-Esparza et al., 2018; Sharman et al., 2014). Among these, aircraft-based observations—especially those from commercial aircraft—have provided extensive in situ turbulence measurements and remain a key component of the global turbulence observation network (Aircraft Meteorological Data Relay; Moninger et al., 2003). The cube root of the eddy dissipation rate (EDR = $\epsilon^{1/3}$), an objective measure of turbulence intensity, is now routinely derived from onboard measurements using an algorithm developed by the National Center for Atmospheric Research (NCAR), which has been implemented on some fleets of United States commercial aircraft (Cornman, 2016; Sharman et al., 2014). However, because commercial aircraft typically operate below ~15 km (e.g., Figure A1 in Hitchcock et al., 2025), data coverage above this altitude (e.g., 15–21 km) remains limited. Furthermore, current US federal guidelines advise pilots to avoid deep convection by at least 20 miles (~32 km) laterally (Federal Aviation Administration, 2022, 2023), making it difficult to obtain turbulence measurements within or near convective storms. Consequently, turbulence observations in the UTLS, particularly those well above 15 km and near deep convection, are likely underrepresented in commercial in situ flight data sets. Again, given the projected increase in convective activity and CIT from future climate studies, in situ observations in high-altitude and convectively influenced regions are crucial for advancing our understanding of turbulence and its broader climate implications.

Dedicated airborne field campaigns provide in situ measurements of turbulence in regions and altitudes not routinely sampled by commercial aircraft. One notable recent campaign providing such measurements is the National Aeronautics and Space Administration (NASA) Dynamics and Chemistry of the Summer Stratosphere
(DCOTSS) campaign, conducted over the central United States during the boreal summers of 2021 and 2022. Its primary goal was to investigate the impacts of tropopause-overshooting storms on stratospheric dynamics, chemistry, and radiation, guided by several core science questions, including “What dynamical mechanisms lead to the irreversible injection of material into the stratosphere by convective storms?” DCOTSS completed 31 flights sampling outflow from deep moist convection as well as quiescent background air masses, reaching altitudes up to ~21.3 km. These observations provide an unprecedented data set for examining the impacts of tropopause-overshooting convection, including impacts in convective outflow surpassing previous records of stratospheric heights hydrated by convection (Homeyer et al., 2023). Since few prior airborne campaigns have conducted extensive in situ sampling of such events (Homeyer et al., 2014; Smith et al., 2017), DCOTSS provides valuable in situ measurements of turbulence associated with deep convection. Additionally, DCOTSS measurements are uniquely suited for investigating the relationship between turbulence and UTLS trace gases. Furthermore, given increasing interest in high-altitude platform stations (HAPS) operating around 20 km MSL (D’Oliveira et al., 2016; Kurt et al., 2021), these airborne in situ observations are particularly beneficial for characterizing turbulence and for improving situational awareness and forecasts critical to ensuring safe and stable operations at high altitudes.

In this study, we use in situ observations of turbulence and water vapor from the DCOTSS field campaign to analyze UTLS turbulence and its potential relationship to water vapor. Here, we seek to address the following questions: (a) Are there specific regions in the UTLS where turbulence is more frequently observed along the DCOTSS flight tracks, either vertically or horizontally? (b) Is there evidence of local instabilities that may have contributed to the observed turbulence? (c) How are the observed turbulence and changes in the UTLS composition related?

## 2. Data and Methods

The DCOTSS field campaign deployed the NASA ER-2 high-altitude aircraft primarily from its mission base in Salina, Kansas, during the summers of 2021 and 2022, with transit flights between Salina, Kansas, and Palmdale, California, the ER-2 home base. A total of 31 (research, test, and transit) flights were flown during the campaign (16 in 2021 and 15 in 2022). The majority of the flights targeted UTLS outflow from deep convection, although some flights sampled the quiescent stratospheric background environment to characterize the North American monsoon circulation (Bowman et al., 2025). The DCOTSS observations analyzed in this study are sourced from the DCOTSS airborne archive (NASA, 2023a), which includes in situ measurements of turbulence, trace gases, aerosols, and meteorological parameters collected by a 12-instrument payload.

### 2.1. DCOTSS Observations

In this study, we used high-frequency (20-Hz) measurements of meteorological parameters along the flight track, including turbulence intensity, potential temperature, and position (latitude, longitude, and altitude), from the Meteorological Measurement System (MMS) (Scott et al., 1990). Turbulence is quantified using the EDR, which provides a measure of turbulence intensity within the inertial subrange (Chan et al., 1998). The EDR in MMS is computed using band-pass filtered (3.2–6 Hz) transverse and vertical components derived from true air speed and aircraft attitude angles (yaw and angle of attack), based on Kolmogorov's second hypothesis of similarity and Taylor's frozen turbulence hypothesis (Hinze, 1975), as detailed in Chan et al. (1998, Equations 1–5). To optimize the signal-to-noise ratio, the 20-Hz EDR data are smoothed to a 1-Hz temporal resolution using a 1-s centered moving average (corresponding to about 200 m). Potential temperature and position data are correspondingly subsampled to the same 1-Hz intervals. Figure 1a shows the horizontal distribution of 1-Hz data counts from all 31 DCOTSS flights, accumulated within 0.25° by 0.25° grid boxes. The maximum number of data points is located in the central United States near the mission base in Salina, Kansas, with data coverage extending across the continental United States, Canada, and the eastern Pacific. During DCOTSS, the ER-2 aircraft reached altitudes as high as 21.28 km during an average 8-hr flight (Figure 1b). Most flights targeting convective outflow were conducted using stair-step patterns composed of constant-altitude flight legs.

We also use 1-Hz water vapor (H₂O) mixing ratio data from the Harvard Herriott Hygrometer (HHH) within the Harvard Water Vapor (HWV, Sargent et al., 2013) instrument to examine possible links between turbulence and atmospheric composition. Additionally, we utilize the feature identifications outlined in Homeyer et al. (2023) to identify measurements of background stratospheric air mass not influenced by overshooting convection. In their
<figure><img src="image_3.png" /><figcaption>**Figure 1.** (a) Horizontal distribution of data counts from 31 DCOTSS flights, accumulated within each 0.25° by 0.25° grid box. (b) Color-coded density plot showing the counts of in situ measurements of turbulence intensity, represented by the cube root of the eddy dissipation rate (EDR), as a function of flight altitude. The mean tropopause height (~14.4 km) is indicated by the horizontal dashed line.</figcaption></figure>

approach, water vapor enhancements indicative of recent convective overshooting material are manually identified and traced back to overshooting convection (detected by radar and satellite observation) using trajectory matching. Specifically, convective features are determined when H₂O enhancements of at least 0.5 ppmv above adjacent background values are coincident with a sufficient number (≥20) of overshoot particles. Each 1-Hz sample is thus categorized according to its convective origin based on these criteria. Samples not classified as having a convective origin are considered nonconvectively influenced or background stratospheric air parcels.

## 2.2. ERA5 Reanalysis

For the analysis of background meteorological conditions during the flights, we use hourly analysis data from the European Center for Medium-range Weather Forecasts (ECMWF) Re-analysis version 5 (ERA5) at a horizontal grid spacing of 0.25° latitude/longitude and 37 pressure levels (Hersbach et al., 2020). The lapse-rate tropopause (World Meteorological Organization, 1957) is identified in each ERA5 temperature profile interpolated to a 200-m
vertical grid spacing. The resulting tropopause heights are then interpolated in time and space to the aircraft location, following previous DCOTSS-related studies (e.g., Gordon et al., 2024; Homeyer et al., 2023, 2024; Li et al., 2023). Additionally, several meteorological variables such as air temperature, horizontal wind, and geo- potential height are used to compute turbulence diagnostics as described in Section 2.4.

## 2.3. Satellite Observations

To examine the spatial relationship between the observed turbulence and convection, we use infrared (IR) brightness temperature from Channel 13 (central wavelength of ~10.3 µm, in the atmospheric window) of the Geostationary Operational Environmental Satellite (GOES) Advanced Baseline Imager (ABI; Schmit et al., 2017). We also use cloud top height retrievals derived by matching IR brightness temperature with temperature profiles from numerical model data (Cooney et al., 2021; Khlopenkov et al., 2021; Yost et al., 2018). Here, convection is identified when the IR brightness temperature $T_b$ in a given (2 km × 2 km) pixel is below 220 K, following Hoffmann and Alexander (2010). For every 1-Hz measurement, we compute the horizontal distance from the aircraft location to the nearest cold pixel ($T_b \le 220$ K) and the vertical distance between the aircraft altitude and the cloud top height at that pixel.

## 2.4. Turbulence Diagnostics and Evaluation Method

Aircraft-scale turbulence has been diagnosed from numerical model data based on the assumption of a downscale cascade process (Cho & Lindborg, 2001; Lindborg, 1999), whereby energy transfers from resolved large-scale atmospheric disturbances to small-scale eddies (e.g., J.-H. Kim et al., 2018; S.-H. Kim et al., 2021; Lee et al., 2022; Sharman & Pearson, 2017; Sharman et al., 2006). Among the various turbulence diagnostics developed either empirically or based on theoretical principles (e.g., S.-H. Kim et al., 2019; Sharman et al., 2006; Sharman & Pearson, 2017), this study utilizes turbulence index versions 1, 2, and 3 (TI1, TI2, TI3; Ellrod & Knapp, 1992; Ellrod & Knox, 2010), along with their components [vertical wind shear (VWS), flow deformation (DEF), divergence (DIV), and divergence tendency (DVT)] as follows:

$$ 
\begin{aligned}
\text{TI1} &= \text{VWS} \times \text{DEF}, \quad \text{VWS} = ((\partial u/\partial z)^2 + (\partial v/\partial z)^2)^{1/2}, \\
\text{DEF} &= ((\partial u/\partial x - \partial v/\partial y)^2 + (\partial v/\partial x + \partial u/\partial y)^2)^{1/2},
\end{aligned}
\quad (1) $$

$$ \text{TI2} = \text{VWS} \times (\text{DEF} - \text{DIV}), \quad \text{DIV} = \frac{\partial u}{\partial x} + \frac{\partial v}{\partial y}, \quad (2) $$

$$ \text{TI3} = \text{TI1} + \text{DVT}, \quad \text{DVT} = C |\text{DIV}_{t_2} - \text{DIV}_{t_1}|, \quad (3) $$

where u and v denote the zonal and meridional wind, respectively, z is the geopotential height, $t_2$ is the time closest to the DCOTSS observation and $t_1$ is 1 hr prior to $t_2$, and the term C is an empirical constant set to 0.01, following Lee et al. (2020, 2023).

Although these indices have been widely used as diagnostics for clear-air turbulence (J.-H. Kim et al., 2018; Sharman & Pearson, 2017; Sharman et al., 2006), they can also be associated with CIT. That is, moist convection can locally increase the vertical shear and deformation of horizontal wind, as well as horizontal divergence (Dauhut et al., 2018; Fovell et al., 2007; Lane et al., 2012; Lane & Sharman, 2008; Sharman & Trier, 2019). Moreover, because gravity waves can be partially resolved in high-resolution model grids, the variability in horizontal wind divergence may potentially reflect gravity wave forcing (e.g., Stephan et al., 2020). S.-H. Kim et al. (2021) further showed that TI3 exhibits high forecasting performance skills for CIT events identified from in situ commercial aircraft data. In this study, three turbulence indices and their components are computed from pressure-level data of hourly ERA5 analysis using 10 vertical layers between 50 and 300 hPa, with a typical spacing of 25 hPa, and interpolated in space and time along the ER-2 flight tracks. These diagnostics and components are then used to analyze the environmental conditions associated with observed turbulence.

To ensure these ERA5-derived turbulence diagnostics are suitable for analyzing observed turbulence environments, we first assess how well they correspond to turbulence measured by MMS during DCOTSS. Three turbulence diagnostics (TI1, TI2, and TI3) are evaluated using probability-of-detection (POD) statistics, following methods described in previous studies (e.g., J.-H. Kim et al., 2018; S.-H. Kim et al., 2021; Lee et al., 2022;
Sharman et al., 2006; Sharman & Pearson, 2017). Null (NIL) and moderate-or-greater (MOG) turbulence events are used in this evaluation. Specifically, the probability that a diagnostic predicts the occurrence of turbulence near observed MOG turbulence events (BODY), and the probability that it does not predict turbulence near observed NIL events (PODN), are computed by comparing turbulence observations to the turbulence potential diagnosed by each index at the nearest grid point. In this study, turbulence events are classified as NIL and MOG turbulence based on EDR thresholds of 0.05 m²/³ s⁻¹ (Lee et al., 2022) and 0.15 m²/³ s⁻¹, respectively. Using PODN, the probability of false detection (POFD) is computed as POFD = 1-PODN. The relative operating characteristic (ROC, Marzban, 2004; Mason & Graham, 1999) curve for each index is constructed by connecting BODY-POFD pairs obtained using a set of thresholds evenly spaced between the minimum and maximum values of the turbulence index. The details can be found in S.-H. Kim et al. (2021). The area under the ROC curve (AUC) is used as skill score, where AUC = 0.5 indicates no skill and AUC = 1 indicates perfect skill, with values above 0.8 generally regarded as practically useful (Sharman et al., 2006).

# 3. Results

## 3.1. Turbulence Occurrence During DCOTSS

Figure 1b shows the vertical distribution of EDR observations from MMS, represented by a color-coded density plot as a function of altitude. DCOTSS observations are concentrated between approximately 14 and 18 km, which are near or above the mean tropopause (~14.4 km, indicated by the horizontal dashed line in Figure 1b). These observations extend well into the stratosphere, reaching up to 9.91 km above the tropopause and covering higher altitudes than those typically measured by commercial aircraft. During DCOTSS, more samples with larger EDR values were collected above the tropopause than below, primarily due to greater sampling density above the tropopause. A normalized distribution of turbulence occurrence across altitudes is examined later. For the remainder of this study, we analyze data collected above 10 km (4.4 km below the mean tropopause altitude), which accounts for 97% of the total observations (total number of samples: 557,027; see Table S1 in Supporting Information S1).

Figure 2a shows a scatter plot of H₂O mixing ratios from HHH in tropopause-relative altitude coordinate, with each point colored by EDR magnitude. For better representation, points with larger EDR values are plotted on top to highlight regions of strong turbulence rather than its occurrence frequency. Nonconvectively influenced air parcels identified by Homeyer et al. (2023) are grouped into 1-km altitude bins, and the median, 10th, and 90th percentiles are calculated at each bin. Curves are then fitted to these statistics to characterize a reference vertical profile of H₂O in the UTLS that is not perturbed by overshooting convection. As expected, H₂O decreases sharply above the tropopause, which separates the moist troposphere from the dry stratosphere. Above the tropopause, the spread between the 10th and 90th percentiles narrows with altitude, indicating less variability in H₂O. We define background dry air as samples with H₂O mixing ratios between the 10th and 90th percentiles (bounded by the two yellow lines in Figure 2a).

In contrast, moist plumes are defined here as samples whose H₂O mixing ratios exceed the 90th percentile (the rightmost yellow line in Figure 2a). As seen by numerous measurements exceeding this threshold, DCOTSS sampled significantly moistened air masses up to ~3.5 km above the tropopause into the stratosphere. Such large H₂O enhancements, substantially exceeding typical background values (e.g., >10 ppmv), can only be attributed to convective storms. Indeed, as reported by Homeyer et al. (2023), stratospheric H₂O enhancements were observed during at least 18 DCOTSS flights, with convective hydration reaching altitudes exceeding previous global records (e.g., Anderson et al., 2012; Hanisco et al., 2007; Khaykin et al., 2009). Turbulent air was sampled across a broad vertical range. Strong turbulence (e.g., EDR ≥ 0.15 m²/³ s⁻¹; light cyan to red) occurs frequently in both moist plumes and background dry air at any given altitude, indicating no clear relationship between H₂O mixing ratio and turbulence intensity.

To further examine the statistical distribution of turbulence, Figure 2b shows probability density functions (PDFs) of EDR for moist plume (red bars) and background dry air (yellow bars) samples above 10 km. Both distributions are right-skewed, reflecting the general rarity of strong turbulence events in the atmosphere. Indeed, across all 1-Hz measurements above 10 km, the fraction with EDR ≥ 0.15 m²/³ s⁻¹ accounts for only 0.339% of the total flight time (see Table S1 in Supporting Information S1). However, turbulence occurs more frequently and with greater intensity in moist plumes than in background dry air, suggesting a potential link between convectively enhanced H₂O and elevated turbulence. Specifically, 0.57% of moist plume samples and 0.2% of background dry air samples
<figure><img src="image_4.png" /><figcaption>**Figure 2.** (a) Scatter plots of water mixing ratio versus tropopause-relative altitude from all DCOTSS flights, with points color-coded by the magnitude of EDR. The red solid line represents the median, while the yellow dashed lines indicate the 10th and 90th percentiles, computed from vertically binned nonconvectively influenced samples. For better visualization, the data are sorted so that points with higher EDR values appear on top. (b) Probability density functions (PDFs) of EDR, categorized into two groups: “moist plume” (red bars), where the H₂O mixing ratio exceeds the 90th percentile, and “background dry air” (yellow bars), where the H₂O mixing ratio falls between the 10th and 90th percentiles.</figcaption></figure>

exhibit EDR ≥ 0.15 m²/³ s⁻¹. Extremely strong turbulence (EDR ≥ 0.3 m²/³ s⁻¹) is also predominantly observed in moist plumes.

The vertical distribution of strong turbulence occurrence (defined as EDR ≥ 0.15 m²/³ s⁻¹) as a function of tropopause-relative altitude is further examined in Figure 3a. Normalized counts are calculated as the fraction of data points exceeding the threshold within each 1-km altitude bin, for moist plume (red) and background dry air (blue) samples. Turbulence within background dry air shows little altitude dependence, with a maximum frequency of ~0.5% occurring within 1 km below the tropopause. In contrast, moist plumes exhibit a distinct peak in turbulence occurrence above the tropopause. Specifically, strong turbulence occurrence shows a broad peak above the tropopause (~2.1% in the 1–2 km layer above the tropopause). These patterns remain within the bootstrap-derived uncertainty bounds based on resampling with half-sized subsets. Previous observational studies have also reported enhanced turbulence occurrence near the tropopause–within 1 km above (e.g., Wolff & Sharman, 2008) or 3 km below (e.g., Hitchcock et al., 2025). Our analysis reveals a similar increase in turbulence occurrence but at somewhat higher altitudes, with the most frequent turbulence occurrence within 2 km above the elevated summertime tropopause (mean height of ~14.4 km) during DCOTSS.
<figure><img src="image_5.png" /><figcaption>**Figure 3.** (a) Vertical distributions of normalized counts of strong-intensity turbulence (EDR ≥ 0.15 m²/³ s⁻¹) as a function of tropopause-relative altitude for moist plume (red) and background dry air (blue) samples. Gray line indicates bins with fewer than 500 total samples (all EDR values). Shaded areas denote 95% confidence intervals derived from 10,000 resampling iterations using randomly selected half-sized subsets. (b) Same as (a), but as a function of cloud top relative altitude, limited to regions within 200 km laterally from convection. (c) Counts (bars) and normalized counts (lines) of strong-intensity turbulence for moist plume (orange bars, red line) and background dry air (gray bars, black line) samples as a function of the minimum lateral distance to convection. Convection is defined where satellite infrared brightness temperature is ≤ 220 K. Normalized counts represent the fraction of samples with EDR ≥ 0.15 m²/³ s⁻¹ in each 1-km altitude bin (a, b) and each lateral distance bin (c).</figcaption></figure>

Additionally, Figure 3b shows the normalized counts of strong turbulence as a function of cloud top-relative altitude, based on DCOTSS observations located within 200 km laterally from convection ($T_b \le 220$ K). As in Figure 3a, moist plumes exhibit enhanced turbulence occurrence compared to background dry air across most altitudes in the UTLS. In particular, the occurrence frequency of strong turbulence is elevated in the layer 1–2 km above the cloud top (~3.8%) as well as below the cloud top altitude (~5%). The higher occurrence of strong turbulence above the cloud top altitude in moist plumes closely aligns with the peak in Figure 3a. This suggests that turbulence in the lower stratosphere is closely associated with deep convection, given the frequent sampling of tropopause-overshooting convection during DCOTSS. The enhanced turbulence above cloud tops may be related to convective gravity waves and their breaking above deep convection, influenced by background static stability and vertical wind shear (Lane & Sharman, 2008). Meanwhile, the elevated occurrence of strong turbulence below the cloud top altitude is also notable and is likely associated with intense turbulent mixing within strong convective updrafts and downdrafts in the cloud (Lane & Sharman, 2014).
<figure><img src="image_6.png" /><figcaption>**Figure 4.** DCOTSS research flight on 8 June 2022: (a) Column-maximum GridRad radar reflectivity at 1500 UTC, (b) the Ellrod turbulence index (TI3) computed from ERA5 reanalysis data at 1500 UTC 15-km altitude, where strong-intensity turbulence was observed. MMS EDR along the flight track from 1400 to 2056 UTC is overlaid on both panels.</figcaption></figure>

Figure 3c shows both the counts (bars) and normalized counts (lines) of strong turbulence as a function of horizontal distance from convection, for the two classified (moist plume and background dry air) samples across six lateral distance bins. In both sample types, the frequency of strong turbulence decreases substantially with increasing distance from convection. The observed peaks in strong turbulence occurrence near convection (Figures 3b and 3c) are consistent with previous studies reporting increased turbulence frequencies near convective clouds (e.g., Atlas & Bretherton, 2023; Baek et al., 2024; Hitchcock et al., 2025; Lane et al., 2012; Podglajen et al., 2017). In the vicinity of convection (within 10 km, the leftmost bin), the normalized count of strong turbulence reaches ~3.1% in moist plume samples, substantially higher than the ~1.2% observed in background dry air. This suggests that turbulence is more frequent near convection and is closely associated with H₂O-enhanced regions near deep convection. At a given distance from convection, turbulence occurs more frequently in moist plumes compared to background dry air, and this pattern persists across the first four distance bins within 100 km. The results indicate that the distance to convection alone is not the only determining factor of turbulence frequency. Stratifying the data by water vapor content, our results suggest that moistened air may be colocated with intense turbulence, implying a potential linkage between convectively hydrated air and strong turbulent regions. Beyond 100 km (the two rightmost bins), strong turbulence occurs more frequently in background dry air than in moist plumes. This is likely driven by nonconvective sources such as mountain waves and jets. For example, the ER-2 aircraft encountered strong turbulence during test flights on 9 June 2021 while sampling in clear-air conditions over mountainous regions, far from deep convection.

Given that DCOTSS frequently sampled significantly moistened air masses that likely originated from deep convection (Figure 2a), the moist plume samples may represent convectively influenced regions and thus serve as a proxy for air affected by deep convection. Therefore, the elevated turbulence observed in these samples is likely associated with various generation mechanisms such as shear and convective instabilities induced by convective outflow, convective gravity wave propagation and its breaking, cloud-interfacial instabilities, and instabilities associated with overshoots and/or ice sublimation (Sharman & Trier, 2019). However, further investigation using numerical simulations should be conducted in future studies.

## 3.2. Environmental Conditions Along DCOTSS Flight Tracks

This subsection examines the environmental conditions that may be associated with the observed turbulence during DCOTSS. To this end, the ERA5 reanalysis data are used to compute the TI indices (TI1, TI2, and TI3) and their components along ER-2 flight tracks, providing context regarding large-scale flow conditions. As an example, Figure 4 shows the column-maximum radar reflectivity and the ERA5-derived TI3 index at 1500 UTC on 8 June 2022 at an altitude of 15 km, near the time and altitude of an encounter with strong turbulence. At that time, deep convection was observed over Oklahoma and Kansas. Between 1430 and 1530 UTC, the ER-2 aircraft repeatedly sampled over northern Oklahoma and encountered strong turbulence near and to the northeast of deep
<figure><img src="image_7.png" /><figcaption>**Figure 5.** Relative operating characteristic (ROC) curves for three turbulence diagnostics computed from ERA5 reanalysis data, evaluated against null (NIL; EDR < 0.05 m²³ s⁻¹) and moderate-or-greater (MOG; EDR ≥ 0.15 m²³ s⁻¹) turbulence events. The area under the ROC curve (AUC) for each diagnostic is shown in the parentheses in the legend. The diagonal dashed line represents the no-skill reference.</figcaption></figure>

convective cells (radar reflectivity ≥40 dBZ; Figure 4a). The TI3 index computed from ERA5 indicates high turbulence potential (TI3 ≥ 1.5 × 10⁻⁶ s⁻¹) across the south-central United States, including northern Oklahoma and western Missouri (Figure 4b). Notably, regions of large TI3 values are located near the ER-2 flight tracks in areas where strong turbulence was observed. In addition, regions of elevated turbulence potential exhibit a circular spatial distribution around deep convection. This spatial agreement suggests that the TI index and its components (VWS, DEF, and DVT) may capture upper-level flow disturbances modulated by vigorous convection (e.g., Trier & Sharman, 2009; Zovko-Rajak & Lane, 2014).

Before examining the environmental conditions associated with the DCOTSS turbulence cases, we evaluate the performance skill of three turbulence indices (TI1, TI2, and TI3) using the POD-based statistics described in Section 2.4. The evaluation is performed against NIL and MOG turbulence from all DCOTSS observations above 10 km (see Table S1 in Supporting Information S1). Figure 5 shows the resulting ROC curves for TI1, TI2, and TI3, together with their AUC values. Each index performs well against the turbulence observations (AUC > 0.87), with TI3 performing best (AUC = 0.881). Turbulence indices derived from ERA5 cannot capture all observed turbulence events, likely because the 0.25° spatial and hourly temporal resolution is insufficient to resolve the intermittent, small-scale nature of turbulence generation mechanisms. Nevertheless, the indices show reasonable agreement with the observations. We proceed to analyze the environmental conditions along the DCOTSS flight tracks using the TI3 and its components.

To investigate how large-scale flow conditions vary with distance to convection, we analyze the PDFs of the TI3 index and its components (VWS, DEF, and DVT) along all flight tracks, grouped by distance from convection. Figure 6 presents these distributions for samples located within 100 km of convection (close-to-convection, red) and those beyond 100 km (far-from-convection, black), with the 95th percentiles indicated by vertical lines. All distributions are positively skewed, indicating that most sampled environments were dominated by relatively weak large-scale flow disturbances. However, close-to-convection samples exhibit consistently higher probabilities at elevated values of VWS, DEF, DVT, and the resulting TI3 than those in samples farther from convection, along with higher 95th percentiles. Results using model-level data from ERA5 reanalysis have larger magnitudes of the TI3 index and its components than pressure-level data but still show a higher probability of elevated values near convection than farther away (not shown). These results suggest that upper-level flow disturbances are more pronounced near convection, likely due to convectively induced outflow and also convective overshoots (e.g., O'Neill et al., 2021; Sharman & Trier, 2019). This is consistent with more frequent occurrence of strong turbulence observed near convection (Figure 2c). Moreover, horizontal divergence in the ERA5 analysis fields may reflect the presence of partially resolved gravity waves (Stephan et al., 2020), and large DVT may then be indicative of gravity waves, which could contribute to some of the observed turbulence. Indeed, during one of the DCOTSS flights (10 August 2021), the pilot reported encountering turbulence while experiencing wave-like motion, suggesting a possible link between gravity waves and the observed turbulence.

We further assess the relevance of the ERA5-derived TI3 index to the observed turbulence intensity by comparing TI3 values along the ER-2 flight paths with collocated MMS EDR measurements. Using the 95th-percentile threshold for each group (1.11 × 10⁻⁶ s⁻¹ for close-to-convection and 0.38 × 10⁻⁶ s⁻¹ for far-from-convection samples), the average EDR is 0.04 m²³ s⁻¹ near convection and 0.026 m²³ s⁻¹ farther away, indicating a tendency for stronger turbulence observed near convection under high TI3 values. These results suggest that enhanced flow disturbances near convection, characterized by stronger VWS, DEF, and DVT (i.e., high TI3), may have contributed to the strong observed turbulence during DCOTSS. Additionally, we compute the mean EDR within three TI3 ranges, defined using the 75th and 95th percentiles. As a result, stronger observed turbulence is associated with higher TI3, with the largest mean EDR values occurring in samples exceeding the 95th percentile of TI3 (see Table S2 in Supporting Information S1). Further investigation using high-resolution numerical simulations is needed to identify the most relevant turbulence generation mechanisms for each observed turbulence case.
<figure><img src="image_8.png" /><figcaption>**Figure 6.** (a–d) Probability density functions (PDFs) of (a) vertical wind shear (VWS), (b) flow deformation (DEF), (c) divergence tendency term (DVT), and (d) Ellrod TI3, computed from ERA5 reanalysis data at the nearest grid points along DCOTSS flight tracks. Vertical dashed lines indicate the 95th percentile values. Red lines represent data samples within 100 km of convection ($T_b \le 220$ K), while black lines represent data samples farther than 100 km from convection.</figcaption></figure>

### 3.3. Impact on Stratospheric Water Vapor

To assess the relevance of turbulence to UTLS composition changes, we examine the relationship between H₂O mixing ratios and EDR. Specifically, we compute the following two quantities over a 21-s temporal window centered on each observation (i.e., 10 s before and after): (a) the variance of H₂O mixing ratio and (b) the Pearson correlation coefficient (Wilks, 1995) between H₂O mixing ratio and EDR (ρ). To minimize the impact of varying potential temperature, we restrict the analysis to constant-altitude flight legs only. Figure 7 shows the relationship between EDR and H₂O variance within stratospheric moist plumes located 0–2 km above the tropopause and near convection (within 15–60 km horizontal distance). We calculate the median EDR (black markers) for each 0.2-ppmv² bin of H₂O variance and fit a linear regression to these median values. Despite the substantial variability in EDR values (gray markers), there is a modest positive correlation between EDR and H₂O variance, suggesting that stronger turbulence tends to occur in regions of higher H₂O variance. Although a clear causal link between turbulence and H₂O cannot be established with this data set alone, Figure 7 suggests a possible role of turbulent mixing in modulating H₂O concentrations in the UTLS.

However, this relationship is not apparent in regions very close to convection (≤15 km) or far from (>60 km) convection (not shown). This may be attributed to the following factors: In regions very close to convection (≤15 km), the environment is likely to be strongly influenced by vigorous convective updrafts and downdrafts, localized detrainment, and microphysical processes such as condensation and evaporation. These processes can lead to substantial water vapor variability that is not necessarily linked to turbulent mixing while also contributing to localized small-scale turbulence in the vicinity of convection. Such complexity may obscure the correlation between H₂O variance and EDR. In contrast, at distances greater than 60 km, the ambient lower stratosphere tends to be drier, exhibiting low H₂O variance. Consequently, the relationship between H₂O variance and EDR is less
<figure><img src="image_9.png" /><figcaption>**Figure 7.** Scatter plot of EDR as a function of H₂O variance for stratospheric moist plume samples near the tropopause (≤2 km of the tropopause) and near-convective regions (15–60 km from convection). Gray markers represent individual EDR values binned at 0.2-ppmv² of H₂O variance. Black markers indicate the median EDR for each bin, and the red line represents the linear regression fit to the median values.</figcaption></figure>

identifiable. Another possible factor is the presence of anisotropic turbulence. Since the EDR estimation adopted in this study fundamentally assumes isotropy, the resulting EDR values may be less representative under anisotropic conditions, which in turn could contribute to a less discernible H₂O variance-EDR relationship. Indeed, when we computed the power spectral densities (PSDs) of 20-Hz transverse air velocity derived from true air speed and yaw angle, the slopes from all 31 DCOTSS flights have a mean of -1.76 and a standard deviation (σ) of 0.69, deviating slightly from the theoretical Kolmogorov -5/3 slope (Kolmogorov, 1941). While the theoretical -5/3 slope lies within 1σ of the mean, only 73.9% of the spectral slopes from the 31 DCOTSS flights fall within this range, suggesting the presence of anisotropic turbulence during some flights. Nonetheless, strong turbulence (EDR ≥ 0.15 m²/³ s⁻¹) cases such as those encountered on 8 June 2022 exhibit a PSD that is close to the theoretical -5/3 slope (see Figure S1 in Supporting Information S1).

To further investigate the relationship between H₂O and turbulence, we analyze the correlation between H₂O mixing ratio (rather than variance) and EDR under different environmental conditions. Figure 8a presents the probability distributions of H₂O-EDR Pearson correlation for two sample

groups within 100 km from convection, categorized by their proximity to the tropopause: ≤2 km above the tropopause (red) and ≥2 km above the tropopause (gray). Both distributions are centered near zero, indicating that H₂O and EDR are generally weakly correlated. However, the distribution for samples located farther above the tropopause exhibits a slight negative shift in its peak, which may suggest that turbulence in the stratosphere is typically associated with the mixing of dry stratospheric air. In contrast, near-tropopause samples exhibit a slight shift toward stronger positive H₂O-EDR correlation, implying that turbulence in this region may be more likely to induce moistening. Approximately 20% of the near-tropopause samples and 13% of the farther samples exhibit positive correlation (ρ ≥ 0.35). In other words, turbulent mixing near convection is more likely to contribute to moistening in the lowest 2 km of the stratosphere, while at higher altitudes, it may instead lead to in-mixing of drier background stratospheric air.

Building on Figure 8a, the moist plume samples located within 2 km above the tropopause are categorized by the magnitude of vertical wind shear, using the 95th percentile threshold (red vertical line in Figure 6a) to distinguish between strong and weak shear conditions. Figure 8b shows the resulting probability distributions of H₂O-EDR correlation for these two categories. Similar to Figure 8a, both distributions exhibit a generally weak H₂O-EDR correlation, indicating that turbulence and H₂O are largely unrelated even under variable shear conditions. However, the distribution shifts toward stronger positive correlation values under strong vertical wind shear (red line of Figure 8b) compared to the weak vertical wind shear condition. Specifically, the percentage of samples with ρ ≥ 0.35 is approximately 27.5% for strong vertical wind shear and 19.3% for weak vertical wind shear. These results imply that enhanced vertical wind shear, potentially associated with convective outflow, may facilitate the moistening of the lower stratosphere through turbulent mixing. Although the overall correlation remains weak, the observed rightward-shift under strong vertical wind shear indicates that turbulence under certain conditions may coincide with localized increases in lower stratospheric water vapor. Therefore, while turbulence does not solely determine stratospheric H₂O variability, its impact appears to be partially modulated by background environmental conditions, with moistening more likely under certain conditions.

While it remains uncertain whether turbulence generated by other dynamical instabilities actively redistributes or enhances water vapor through mixing, or whether mixing results directly from the stratospheric hydration processes near convection (e.g., Gordon et al., 2024; Homeyer et al., 2024; Smith et al., 2017), our results offer some insights into the relationship between turbulence and H₂O. Specifically, turbulence near convection tends to coincide with moistening in the lowermost stratosphere (within 2 km above the tropopause), whereas at higher altitudes, it is more often associated with decreased H₂O. Moreover, the impact of mixing on H₂O may depend not only on altitude—such as proximity to tropopause and/or cloud top—but also on the surrounding environmental conditions. A study utilizing multiscale numerical simulation is currently underway to better understand the causal relationship between turbulence and H₂O.
<figure><img src="image_10.png" /><figcaption>**Figure 8.** PDFs of the H₂O-EDR Pearson correlations for DCOTSS data within 100 km of convection. (a) Stratospheric data classified based on proximity to the tropopause: near the tropopause (≤2 km, red) and farther from the tropopause (>2 km, gray). (b) Stratospheric moist plume samples near the tropopause (≤2 km), further categorized by strong (red) and weak (gray) VWS using the 95th percentiles of VWS (red line in Figure 6a) as the threshold. Vertical dashed lines indicate the 95th percentile of each PDF.</figcaption></figure>

4. Summary and Discussion

This study investigates UTLS turbulence and its relationship with H₂O using high-frequency in situ airborne observations from the DCOTSS field campaign conducted over the United States in the summers of 2021 and 2022. While the DCOTSS observations do not cover the full range of UTLS environments, they span a broad range of meteorological conditions across tropical, subtropical and midlatitude regions. Notably, the data set includes extensive measurements of near-cloud turbulence, enabled by a targeted sampling strategy focused on convective overshooting plumes. We analyze high-altitude turbulence (as represented by EDR) and H₂O observations reaching up to 21.28 km, which are altitudes rarely covered by conventional aircraft-based turbulence measurements due to the operating limits of commercial aircraft.

Throughout the campaign, the NASA ER-2 aircraft frequently sampled convectively moistened air masses from the tropopause up to ~3.5 km into the lower stratosphere. Strong turbulence (EDR ≥ 0.15 m²/³ s⁻¹) is most frequently observed within 2 km above the tropopause, particularly in the 1–2 km layer above cloud tops and within 10 km laterally from convection. These occurrences are particularly frequent in moist plume samples, which are characterized by enhanced water vapor mixing ratios, compared to background levels. Moist plumes remain more turbulent than background dry air out to 100 km laterally, likely due to convectively induced turbulence and its role in mixing convectively moistened air.

By examining environmental conditions associated with observed turbulence during DCOTSS, we found that regions located within 100 km of convection generally exhibit stronger vertical wind shear as well as flow deformation and horizontal divergence, resulting in higher TI3 values compared to more distant regions. The favorable environmental factors captured by these large-scale diagnostics are consistent with the elevated frequency of strong turbulence observed near convection. Nevertheless, further investigation through multiscale numerical modeling is required to examine the generation mechanisms responsible for each turbulence case. Additionally, given that our analysis uses the distance to nearest cold pixel at the time closest to the observation, further work is needed to account for the convective maturity and the origin of the sampled air.

The ERA5-derived turbulence diagnostics (TI1, TI2, and TI3), which include VWS, DEF, and DVT, reasonably captured the environments conducive to observed turbulence, as exemplified by AUC values greater than 0.87. This

suggests that these diagnostics, commonly used to forecast turbulence at commercial cruising altitudes, may remain feasible for diagnosing high-altitude turbulence. Considering that current operational turbulence forecasts, based on multidiagnostic frameworks (J.-H. Kim et al., 2018; S.-H. Kim et al., 2021; Lee et al., 2022; Sharman & Pearson, 2017), are produced for flight levels up to 48 kft (https://aviationweather.gov/gfa/#turb), further evaluation of turbulence diagnostics, including those used in this study, at higher altitudes should be conducted. The use of comprehensive high-altitude turbulence observations—including those influenced by moist convection, jet/frontal systems, and mountains—is also essential for advancing turbulence diagnosis and forecasting in the lower stratosphere. Such improvements could be particularly relevant for HAPS, a growing focus of the aviation community, which operate in this altitude range and may be vulnerable to turbulence, a key hazard for long-duration flight and platform stability.

Furthermore, near the tropopause as well as close to convection, strong turbulence is frequently accompanied by large variability in H₂O mixing ratios, suggesting a potential link between turbulent mixing and localized variability in lower stratospheric humidity. Pearson correlation analysis between EDR and H₂O mixing ratio indicates
that turbulence intensity (i.e., the magnitude of EDR) does not appear to be strongly related to H₂O mixing ratio. However, our results indicate that positive H₂O-EDR correlations (ρ ≥ 0.35) occur more frequently under specific conditions, such as near the tropopause (~20% of samples) and under strong vertical wind shear (~27.5%). These findings imply that turbulence may contribute to stratospheric moistening under certain spatial and/or dynamical conditions. Further work utilizing multiscale numerical modeling will be needed to clarify the causal linkage between turbulence and stratospheric H₂O.

This study utilizes high-altitude in situ observations, including frequent sampling near deep convection, to examine turbulence and its potential connection to H₂O at altitudes that have been underexplored. Although commercial aircraft have provided routine turbulence observations, such data are rarely available at altitudes above 15 km and in environments influenced by tropopause-overshooting deep convection due to operational altitude limits and the intentional avoidance of deep convective systems. While some features of the observed turbulence are consistent with previous observational findings (e.g., Hitchcock et al., 2025; Wolff & Sharman, 2008), the DCOTSS data set also reveals distinct patterns, including more frequent occurrence 1–2 km above cloud tops rather than directly at the cloud top. These differences suggest that DCOTSS observations may offer complementary insights into UTLS turbulence characteristics, which merit further investigation through targeted case studies. In addition, these turbulence observations can contribute to the development and improvement of turbulence forecasting at these flight levels, which may be particularly useful for supporting HAPS operations.

The relationship between turbulence and H₂O remains underexplored with in situ measurements in the UTLS, although some studies have analyzed turbulence and water vapor observations (e.g., Heller et al., 2017). In this context, the present study provides important observational insights into their potential linkage. Our results, which suggest a preferential role of turbulence in modulating stratospheric H₂O under specific conditions, warrant further investigation. Moreover, identifying environmental conditions in which the turbulence-H₂O relationship is enhanced or suppressed would help clarify the underlying mechanisms. Such efforts could improve our understanding of how changes in larger-scale atmospheric conditions influence water vapor distribution through small-scale turbulence-driven processes, with potential implications for climate.

Meanwhile, given that the DCOTSS sampling strategy is inherently biased toward convectively influenced environments, the turbulence statistics and potential relevance to stratospheric H₂O derived from the DCOTSS data set may not fully reflect the typical climatological conditions, particularly in regions dominated by nonconvective sources such as jet/frontal systems and mountains. Moreover, given the spatial (i.e., central United States) and temporal (i.e., summer) focus of the campaign, further observational studies are necessary to explore turbulence characteristics and their influence on UTLS composition across different geographic regions and seasons.

## Conflict of Interest

The authors declare no conflicts of interest relevant to this study.

## Data Availability Statement

All DCOTSS aircraft data (https://doi.org/10.5067/ASDC/DCOTSS-Aircraft-Data_1, NASA, 2023a) and satellite and radar data (https://doi.org/10.5067/ASDC/DCOTSS-Radar-Satellite-Data_1, NASA, 2023b) are available from the NASA Atmospheric Sciences Data Center. The ERA5 reanalysis data on pressure levels can be obtained from the Climate Data Store (https://doi.org/10.24381/cds.bd0915c6, Hersbach et al., 2023).

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