From Flight Logs to Atmospheric Science: Paragliders as Convection Sensors for Identifying Thermal Predictors
Abstract
Atmospheric thermal convection drives boundary-layer dynamics and vertical exchange of heat, moisture, and momentum, yet fundamental questions about thermal structure remain open due to limited in situ observational coverage. We introduce a novel high-resolution observational dataset for atmospheric convection based on paragliding flight logs collected over metropolitan France during 2017-2024. Paragliders probe thermal updrafts by circling within them while carrying GPS variometers that record position and altitude; each climbing segment samples the vertical velocity field within a thermal column. Aggregated across 1.47 million climbing segments from 110,730 flights, this dataset provides unprecedented spatial coverage and temporal resolution. To demonstrate the value of this observational resource, we extract three physically distinct observables from climbing segments and characterize their dependence on terrain, season, time of day, cloud state, and soil moisture. Coupling the paragliding observations with global atmospheric reanalysis data (ERA5, 0.25 degrees hourly) through a regression framework against 118 physically interpretable predictors, we identify leading atmospheric predictors of these observables. The empirical relationship between ceiling height and temperature-dew point depression matches the theoretical lifting condensation level scaling with striking precision supporting our methodology. Boundary-layer height emerges as the leading independent predictor of both ceiling height and thermal strength across all terrains and seasons, while vertical-velocity variability is controlled by surface heat-flux and wind variables. Taken together, our results highlight the utility of crowdsourced flight-log data for investigating atmospheric convection.
Related
- Energy cascades in atmospheric moist convection
- Rainfall isotopic variability in northern Central America driven by regionally-organized deep atmospheric convection and airmass rainout
- A structural analysis of U.S. homelessness through Markov random fields: Identifying predictors and potential causal links through permutation tests
- Paragliders as probes of the Alpine boundary layer: sink polars from 86 000 flight logs and the flow field of a valley
- Mesospheric Gravity Waves in the OH Airglow Layer Generated by Winter Convection during an East Asian Winter Storm
BibTeX
@misc{hernandezaguayo2026from,
title = {{From Flight Logs to Atmospheric Science: Paragliders as Convection Sensors for Identifying Thermal Predictors}},
author = {César Hernández-Aguayo and Matthieu Cristelli and Michael Benzaquen},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2608.00241},
eprint = {2608.00241},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.00241},
}
RIS
TY - UNPB
TI - From Flight Logs to Atmospheric Science: Paragliders as Convection Sensors for Identifying Thermal Predictors
AU - César Hernández-Aguayo
AU - Matthieu Cristelli
AU - Michael Benzaquen
PY - 2026
DA - 2026/09/07
PB - arXiv
DO - 10.48550/arxiv.2608.00241
UR - https://arxiv.org/abs/2608.00241
AB - Atmospheric thermal convection drives boundary-layer dynamics and vertical exchange of heat, moisture, and momentum, yet fundamental questions about thermal structure remain open due to limited in situ observational coverage. We introduce a novel high-resolution observational dataset for atmospheric convection based on paragliding flight logs collected over metropolitan France during 2017-2024. Paragliders probe thermal updrafts by circling within them while carrying GPS variometers that record position and altitude; each climbing segment samples the vertical velocity field within a thermal column. Aggregated across 1.47 million climbing segments from 110,730 flights, this dataset provides unprecedented spatial coverage and temporal resolution. To demonstrate the value of this observational resource, we extract three physically distinct observables from climbing segments and characterize their dependence on terrain, season, time of day, cloud state, and soil moisture. Coupling the paragliding observations with global atmospheric reanalysis data (ERA5, 0.25 degrees hourly) through a regression framework against 118 physically interpretable predictors, we identify leading atmospheric predictors of these observables. The empirical relationship between ceiling height and temperature-dew point depression matches the theoretical lifting condensation level scaling with striking precision supporting our methodology. Boundary-layer height emerges as the leading independent predictor of both ceiling height and thermal strength across all terrains and seasons, while vertical-velocity variability is controlled by surface heat-flux and wind variables. Taken together, our results highlight the utility of crowdsourced flight-log data for investigating atmospheric convection.
ER -