The planetary boundary layer (PBL) governs the vertical transport of mass, momentum, and moisture between the surface and the free atmosphere, and thus the determination of PBL height (BLH) is recognized as crucial for air quality, weather, and climate analysis. Although reanalysis products can provide important insight into the global view of BLH in a seamless way, the BLH observed in situ on a global scale remains poorly understood due to the lack of high-resolution (1 or 2 s) radiosonde measurements. The present study attempts to establish a near-global BLH climatology at synoptic times (00:00 and 12:00 UTC) and in the daytime using high-resolution radiosonde measurements over 300 radiosonde sites worldwide for the period 2012 to 2019, which is then compared against the BLHs obtained from four reanalysis datasets, including ERA5, MERRA-2, JRA-55, and NCEP-2. The variations in daytime BLH exhibit large spatial and temporal dependence, and as a result the BLH maxima are generally discerned over the regions such as the western United States and western China, in which the balloon launch times mostly correspond to the afternoon. The diurnal variations in BLH are revealed with a peak at 17:00 local solar time (LST). The most promising reanalysis product is ERA5, which underestimates BLH by around 130 m as compared to radiosondes released during daytime. In addition, MERRA-2 is a well-established product and has an underestimation of around 160 m. JRA-55 and NCEP-2 might produce considerable additional uncertainties, with a much larger underestimation of up to 400 m. The largest bias in the reanalysis data appears over the western United States and western China, and it might be attributed to the maximal BLH in the afternoon when the PBL has risen. Statistical analyses further indicate that the biases of reanalysis BLH products are positively associated with orographic complexity, as well as the occurrence of static instability. To our best knowledge, this study presents the first near-global view of high-resolution radiosonde-derived boundary layer height and provides a quantitative assessment of the four frequently used reanalysis products.

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 PBL height (PBLH); suffering from low spatial coverage and temporal resolution, the radiosonde data are incapable of providing a 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 machine learning algorithms to integrate high-resolution radiosonde measurements, ERA5 reanalysis, and the Global Land Data Assimilation System (GLDAS) product. This dataset covers the period from 2011 to 2021 with a temporal resolution of 3 h and a horizontal resolution of $\mathrm{0.25}{}^{\circ }\times \mathrm{0.25}{}^{\circ }$ . The radiosonde dataset contains 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 in terms of both 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), and are of significance for a multitude of scientific research endeavors and applications, including air quality, convection initiation, climate, and climate change, to name but a few.

Kelvin–Helmholtz instability (KHI) is most likely to be the primary source for clear-air turbulence, which is of importance in pollution transfer and diffusion and aircraft safety. It is indicated by the critical value of the dimensionless Richardson (Ri) number, which is predicted to be $\mathrm{1}/\mathrm{4}$ from linear stability analysis. However, Ri is fairly sensitive to the vertical resolution of the dataset; a higher resolution systematically leads to a finer structure. The study aims to evaluate the performance of ERA5 reanalysis in determining the spatial–temporal variabilities in subcritical Ri by comparing it against a near-global high-resolution radiosonde dataset during the years 2017 to 2022 and further highlights the global climatology and dynamical environment of subcritical Ri. Overall, the occurrence frequency of $\mathit{\text{Ri}}<\mathrm{1}/\mathrm{4}$ is inevitably underestimated by the ERA5 reanalysis over all climate zones at all heights from the near-ground atmosphere up to 30 km, compared to radiosonde, due directly to the severe underestimation in wind shears. Otherwise, the occurrence frequency of Ri<1 in ERA5 is climatologically consistent with that from $\mathit{\text{Ri}}<\mathrm{1}/\mathrm{4}$ in radiosondes in the free troposphere, especially over the midlatitude and subtropics in the Northern Hemisphere and Southern Hemisphere. Therefore, we argue that the threshold value of Ri could be approximated as 1 rather than $\mathrm{1}/\mathrm{4}$ when using ERA5-based Ri as a proxy for KHI. The occurrence frequency of subcritical Ri revealed by both datasets exhibits significant seasonal cycles over all climate zones. In addition, it is positively correlated with the standard derivation of orography at low levels and is exceptionally strong over the Niño-3 region at heights of 6–13 km. Furthermore, a high occurrence of subcritical Ri would likely be accompanied by strong wind speeds and intensive orographic or non-orographic gravity waves.

The westerly phase of the stratospheric Quasi-Biennial Oscillation (QBO) was unprecedentedly interrupted by an easterly jet at around 22 km during boreal wintertime in 2015/2016 and 2019/2020. Many studies have investigated the role of planetary waves during these disruptions. However, the behavior of gravity waves (GWs) during these disruptions is still unclear. In this paper, we investigated the characteristics of stratospheric GWs during QBO disruptions by analyzing the U.S. high-resolution radiosonde data from 1998 to 2021 from three equatorial stations. The disruptions were separated into three stages: the westerly zonal wind decreasing stage, the easterly zonal wind developing stage, and the westerly zonal wind recovery stage. Notably, the tropical stratospheric GWs’ total energy densities were enhanced during all three stages of both events compared to those in typical years. The low-tropospheric convection, the middle-tropospheric jet, and the low-stratospheric vertical wind shear were statistically associated with the stratospheric GW variations. A quantitative analysis further indicated that the low-tropospheric convection activity, tropospheric jets, and wind shears in the lower stratosphere could well explain the variations in the stratospheric GWs in the westerly zonal wind decreasing and easterly zonal wind developing stages by applying a partial least squares regress analysis.