A merged continental planetary boundary layer height dataset based on high-resolution radiosonde measurements, ERA5 reanalysis, and GLDAS

Abstract. The planetary boundary layer (PBL) is the lowermost part of the troposphere that governs the exchange of momentum, mass and heat between surface and atmosphere. To date the radiosonde measurements have been extensively used to estimate PBLH; suffering from low spatial coverage and temporal resolution, the radiosonde data is incapable of providing the diurnal description of PBLH across the globe. To fill this data gap, this paper aims to produce a temporally continuous PBLH dataset during the course of a day over the global land by applying the machine learning algorithms to integrate high-resolution radiosonde measurements, ERA5 reanalysis, and GLDAS product. This dataset covers the period from 2011 to 2021 with a temporal resolution of 3-hour and a horizontal resolution of 0.25°×0.25°. The radiosonde dataset contained around 180 million profiles over 370 stations across the globe. The machine learning model was established by taking 18 parameters derived from ERA5 reanalysis and GLDAS as input variables while the PBLH biases between radiosonde observations and ERA5 reanalysis were used as the learning targets. The input variables were presumably representative regarding the land properties, near-surface meteorological conditions, terrain elevations, lower tropospheric stabilities, and solar cycles. Once a state-of-the-art model had been trained, the model was then used to predict the PBLH bias at other grids across the globe with parameters acquired or derived from ERA5 and GLDAS. Eventually, the merged PBLH can be taken as the sum of the predicted PBLH bias and the PBLH retrieved from ERA5 reanalysis. Overall, this merged high-resolution PBLH dataset was globally consistent with the PBLH retrieved from radiosonde observations both in magnitude and spatiotemporal variation, with a mean bias of as low as –0.9 m. The dataset and related codes are publicly available at https://doi.org/10.5281/zenodo.6498004 (Guo et al., 2022), which are of significance for a multitude of scientific research and applications, including air quality, convection initiation, climate and climate change, just to name a few.