平流层飞艇因其卓越性能，在航空航天领域的飞行任务中展现出巨大潜力。但平流层环境的复杂性对飞行安全构成严峻挑战，因此提前做好环境预测，规避飞行风险，对飞艇顺利完成飞行任务至关重要。尽管现有的热力学模型已为飞艇设计提供了理论基础，但其分析仍不够完善，因此本文引入云顶高度和云顶温度两项关键参数，深入分析其对飞艇热平衡的影响效果：云顶高度决定了云层与飞艇之间的相对位置，云顶温度则直接反映了云层的热力学状态，两者均会影响飞艇的辐射交换和热平衡状态。基于此分析强调了将这些因素纳入热力学模型的重要性和紧迫性，为未来飞艇热力学模型的优化提供了新思路，揭示了研究云层分布特征的关键意义。此外，还强调了青藏高原地区作为天然实验室的独特优势，并展开具体数据分析。本文基于 CLARA-A3数据集，分析了 2015-2020年间青藏高原地区的历史气象云层观测数据，重点研究了云顶高度和云顶温度两参数的空间分布特征、日均值和极值情况、云量面积占比情况及两参数之间的相关性分析。结果表明，云顶高度呈西北低东南高的空间分布特征，而云顶温度则呈现西高东低的变化趋势；同时，7-9月，云顶高度全年最高，云顶温度全年最低；云顶参量的年度变化规律也得到验证；进一步分析表明，青藏高原地区存在极端的超高云和超低温气象现象，存在观测点的日均云顶高度超 18 km，云顶温度低于-83 ℃的现象，且多发生在 7-9 月；两变量经 Spear‐man相关系数分析存在中等强度的负相关性，其中7-9月为强负相关性，这为进一步量化云层对飞艇影响因素提供了重要数据支持。经上述研究表明，云层对飞艇的潜在威胁不可忽视，尤其在7-9月，平流层飞艇的部署准备工作需重点关注当地气象云参量的变化。提前掌握云层的观测数据，实施气象预测准备工作，是确保飞艇安全飞行的关键因素之一。本研究首次将真实气象数据分析应用于飞艇飞行环境评估，验证了数据分析技术的可行性，并强调了实测数据在模型验证中的关键作用，为完善平流层飞艇热力学模型提供了新的研究视角。未来，随着动态预测模型的持续优化，飞艇在复杂气象环境中的安全性有望得到显著提升。

Stratospheric airships，due to their excellent performance，exhibit great potential in flight missions within the aerospace field. However，the complexity of the stratospheric environment presents significant challenges to flight safety. Therefore，conducting environmental forecasts in advance to mitigate flight risks is crucial for the successful completion of airship missions. Although existing thermodynamic models have provided a theoretical foundation for airship design，their analysis remains insufficient. In this paper，two key parameters，cloud top height and cloud top temperature，are introduced to deeply analyze their effects on the airship's thermal balance. Cloud top height determines the relative position between the cloud layer and the airship，while cloud top temperature directly reflects the thermodynamic state of the cloud layer. Both parameters influence the radiative exchange and thermal balance of the airship. Based on this analysis，the importance and urgency of incorporating these factors into thermodynamic models are emphasized，offering new insights for the optimization of future air‐ ship thermodynamic models and revealing the significance of studying cloud layer distribution characteristics. Additionally，the unique advantages of the Qinghai-Xizang（Tibet）Plateau as a natural laboratory are highlight‐ ed，and specific data analysis is conducted. This paper analyzes historical meteorological cloud observation data from 2015 to 2020 in the Qinghai-Xizang（Tibet）Plateau region using the CLARA-A3 dataset，focusing on the spatial distribution characteristics，daily mean and extreme values，cloud area proportions，and the correlation analysis between cloud top height and cloud top temperature. The results show that cloud top height exhibits a spatial distribution pattern of lower in the northwest and higher in the southeast，while cloud top temperature shows a trend of higher in the west and lower in the east. During the period from July to September，cloud top height reaches its highest annual value，and cloud top temperature reaches its lowest. The annual variation pattern of the cloud top parameters is also confirmed. Further analysis indicates the presence of extreme meteorological phenomena，such as ultra-high clouds and extremely low temperatures，with daily cloud top heights exceeding 18 km and cloud top temperatures below -83 ℃，which mostly occur between July and September. A Spearman correlation coefficient analysis reveals a moderate negative correlation between the two variables，with a strong negative correlation during the July-September period. This provides important data support for further quantifying the influence of cloud layers on airship performance. The study shows that the potential threat of cloud layers to air‐ ships cannot be ignored，particularly during the July-September period，when the deployment preparation for stratospheric airships should focus on the changes in local meteorological cloud parameters. Acquiring cloud observation data in advance and implementing meteorological forecasting preparation are key factors in ensuring the safe flight of airships. This research is the first to apply real meteorological data analysis to airship flight environment assessment，verifying the feasibility of data analysis techniques，and emphasizing the critical role of observational data in model validation，providing a new research perspective for improving the thermodynamic models of stratospheric airships. In the future，with the continuous optimization of dynamic prediction models，the safety of airships in complex meteorological environments is expected to be significantly improved.