Measurement Uncertainty in Planetary Boundary Layer Height via Model-Based Monte Carlo Simulation

The Planetary Boundary Layer (PBL), the lowest part of the atmosphere, governs the exchange of energy and moisture and is the zone where the highest concentrations of pollutants occur before reaching the free troposphere. The Planetary Boundary Layer Height (PBLH) is therefore a key variable in many 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 methods, and the Richardson method. The framework builds on three components. First, it uses the GCOS Reference Upper‑Air Network (GRUAN) Data Product (GDP), which provides traceable uncertainty estimates for the variables required in PBLH retrievals. Second, a Monte Carlo approach is used to propagate uncertainties and produce synthetic profile ensembles, allowing for an independent validation of various PBLH detection algorithms. A Monte Carlo scheme is chosen over the GUM framework, as the latter is analytically challenging or often inadequate for the non-analytical derivatives required by PBLH methods. Third, it employs a statistical model that captures the structure of atmospheric profiles and enables the generation of physically plausible synthetic vertical profiles of the atmosphere consistent with both observations and their uncertainties. This method enables a systematic comparison of PBLH retrieval techniques, establishing confidence for their performance and revealing how specific atmospheric conditions modulate uncertainty.