AI 中文总结
研究利用GRUAN数据产品、状态空间模型和蒙特卡罗方法，将探空仪测量不确定性传播到PBLH估计中，不仅能提供不确定性估计，还在案例研究中显示出PBLH检测稳健性增加的迹象。
AI 中文摘要
行星边界层（PBL）控制着能量和水分的交换，并且在污染物混入自由对流层之前，其污染物浓度最高。因此，PBL高度（PBLH）是气象和空气质量应用中的关键变量。尽管有多种从大气观测中推导PBLH的方法，但相关不确定性很少被量化。本研究提出一种方法，将探空仪测量不确定性传播到从包括气块法、基于梯度的方法和理查森数法等最新反演方法获得的PBLH估计中。该框架依赖三个组件：首先使用GCOS参考高空网络（GRUAN）数据产品，它为PBLH反演所需的所有变量提供可溯源的不确定性估计；其次采用状态空间模型，捕捉大气剖面结构并生成与观测及其不确定性一致的物理上合理的模拟垂直剖面；第三，使用蒙特卡罗方法将测量不确定性传播到PBLH估计中，完善反演并量化其不确定性。除了提供不确定性估计外，该方法在几个案例研究中还显示出在PBLH检测中稳健性增加的初步迹象，特别是在基于标准梯度的方法对测量不确定性敏感的情况下。
英文摘要
The Planetary Boundary Layer (PBL) governs the exchange of energy and moisture and hosts the highest concentrations of pollutants before they mix into the free troposphere. The height of the PBL (PBLH) is therefore a key variable in meteorological and air-quality applications. Despite the wide range of methods available to derive PBLH from atmospheric observations, the associated uncertainties are rarely quantified. This study presents a methodology for propagating radiosonde measurement uncertainty into PBLH estimates obtained from state-of-the-art retrieval methods, including the parcel method, gradient-based methods, and the Richardson-number method. The framework relies on three components. First, it uses the GCOS Reference Upper-Air Network (GRUAN) Data Product, which provides traceable uncertainty estimates for all variables required in PBLH retrievals. Second, it employs a state-space model that captures the structure of atmospheric profiles and enables the generation of physically plausible simulated vertical profiles consistent with both observations and their uncertainties. Third, a Monte Carlo approach is used to propagate measurement uncertainty into the PBLH estimates, refining the retrieval and quantifying its uncertainty. Beyond providing uncertainty estimates, the methodology also shows preliminary signs of increased robustness in PBLH detection across several case studies, particularly in situations where standard gradient-based methods exhibit sensitivity to measurement uncertainty.
Comments41 pages, 16 figures
