An ensemble method for improving the estimation of planetary boundary layer height from radiosonde data

Abstract

Abstract. The planetary boundary layer (PBL) height (PBLH) is an important parameter for weather, climate, and air quality models. Radiosonde is one of the most commonly used instruments for PBLH determination and is generally accepted as a standard for other methods. However, mainstream approaches for the estimation of PBLH from radiosonde present some uncertainties and even show disadvantages under some circumstances, and the results need to be visually verified, especially during the transition period of different PBL regimes. To avoid the limitations of individual methods and provide a benchmark estimation of PBLH, we propose an ensemble method based on high-resolution radiosonde data collected in Beijing in 2017. Seven existing methods including four gradient-based methods are combined along with statistical modification. The ensemble method is verified at 08:00, 14:00, and 20:00 Beijing time (BJT = UTC+8), respectively. The overestimation of PBLH can be effectively eliminated by setting thresholds for gradient-based methods, and the inconsistency between individual methods can be reduced by clustering. Based on the statistics of a 1-year observational analysis, the effectiveness (E) of the ensemble method reaches up to 62.6 %, an increase of 6.5 %–53.0 % compared to the existing methods. Nevertheless, the ensemble method suffers to some extent from uncertainties caused by the consistent overestimation of PBLH, the profiles with a multi-layer structure, and the intermittent turbulence in the stable boundary layer (SBL). Finally, this method has been applied to characterize the diurnal and seasonal variations of different PBL regimes. Particularly, the average convective boundary layer (CBL) height is found to be the highest in summer, and the SBL is lowest in summer with about 200 m. The average PBLH at the transition stage lies around 1100 m except in winter. These findings imply that the ensemble method is reliable and effective.