发表机构
Econophysics Lab, Institut Louis Bachelier; LadHyX UMR CNRS 7646, École Polytechnique, Institut Polytechnique de Paris; Capital Fund Management（经济物理学实验室，路易·巴舍利耶研究所; 拉迪赫克斯联合研究实验室（CNRS 7646），巴黎综合理工学院，巴黎理工学院; 资本基金管理公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究利用法国2017-2024年的滑翔伞飞行日志构建高分辨率大气对流数据集，结合ERA5数据和正交回归框架，确定了大气对流的关键预测因子，凸显了众包飞行数据的应用价值。
AI 中文摘要
大气热对流驱动边界层动力学以及热量、水汽和动量的垂直交换，但由于原位观测覆盖有限，关于热结构的基础问题仍未解决。我们引入一种基于2017-2024年法国大都市地区收集的滑翔伞飞行日志的新型高分辨率大气对流观测数据集。滑翔伞通过在热上升气流中盘旋来探测热气流，同时携带记录位置和高度的GPS变率计；每一段爬升过程都会采样热柱内的垂直速度场。该数据集汇总了110730次飞行中的147万段爬升数据，提供了前所未有的空间覆盖范围和时间分辨率。为了展示该观测资源的价值，我们从爬升段中提取三个物理上不同的可观测变量，并表征它们对地形、季节、一天中的时间、云状态和土壤湿度的依赖性。通过正交回归框架将滑翔伞观测与全球大气再分析数据（ERA5，0.25度每小时）相结合，针对118个物理解释性预测因子，我们确定了这些可观测变量的主要大气预测因子。云底高度与温度-露点差之间的经验关系与理论抬升凝结高度标度惊人地吻合，支持了我们的方法。边界层高度成为所有地形和季节中云底高度和热强度的主要独立预测因子，而垂直速度变异性则由地表热通量和风变量控制。总之，我们的结果强调了众包飞行日志数据在研究大气对流方面的实用性。
英文摘要
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.
Comments13 pages, 7 figures
