Statistical Modelling of Planetary Boundary Layer Height and Its Measurement Uncertainty Using GRUAN Profiles

Abstract

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.