Evaluation of the Planetary Boundary Layer Height in China Predicted by the CMA-GFS Global Model

Abstract

The key role of the planetary boundary layer height (PBLH) in pollution, climate, and model forecasting has long been recognized. However, the observed PBLH has rarely been used to evaluate numerical weather prediction models in China. We compared the temporal and spatial characteristics of the bias in the PBLH in China predicted by the CMA-GFS model with vertical high-resolution sounding data and Global Positioning System occultation data from 2019 to 2020. We found that: (1) The PBLH in East China is systematically underestimated by the CMA-GFS model. The bias mainly results from the underestimation of the wind shear in the boundary layer, a smaller sensible heat flux near the surface, and a lower surface temperature. The combined effects of these factors inhibit the boundary layer from developing to a higher height, although the most important contributor is the small sensible heat flux. (2) There is a systematic overestimation of the PBLH over the Tibetan Plateau throughout the year. The bias is mainly a result of the smaller buoyancy, higher wind shear, and larger sensible heat flux forecast by the CMA-GFS model, which drive the boundary layer to develop to a significantly deeper height than the observations. This bias in the CMA-GFS model is mainly caused by the bias in the sensible heat flux and wind shear forecasts. In contrast, the CMA-GFS model underestimates the PBLH in the Tarim Basin. Our preliminary analysis shows that the boundary layer forecasted is unable to develop because the buoyancy effect of the model is too strong. Therefore, the bias of the predicted PBLH by the CMA-GFS model in China is mainly caused by inaccuracies in the sensible heat flux and wind shear forecasts.

A Comparative Study on Four Methods of Boundary Layer Height Calculation in Autumn and Winter under Different PM2.5 Pollution Levels in Xi’an, China

“…Consequently, the NMRF scheme performs better than MRF in convective conditions. However, the NMRF may show a considerably underestimation in the BLH modeling [44].…”