Planetary boundary layer (PBL) height plays a significant role in climate modeling, weather forecasting, air quality prediction, and pollution transport processes. This study examined the climatology of PBL-associated meteorological parameters over the Korean peninsula and surrounding sea using data from the ERA5 dataset produced by the European Centre for Medium-range Weather Forecasts (ECMWF). The data covered the period from 2008 to 2017. The bulk Richardson number methodology was used to determine the PBL height (PBLH). The PBLH obtained from the ERA5 data agreed well with that derived from sounding and Global Positioning System Radio Occultation datasets. Significant diurnal and seasonal variability in PBLH was observed. The PBLH increases from morning to late afternoon, decreases in the evening, and is lowest at night. It is high in the summer, lower in spring and autumn, and lowest in winter. The variability of the PBLH with respect to temperature, relative humidity, surface pressure, wind speed, lower tropospheric stability, soil moisture, and surface fluxes was also examined. The growth of the PBLH was high in the spring and in southern regions due to the low soil moisture content of the surface. A high PBLH pattern is evident in high-elevation regions. Increasing trends of the surface temperature and accordingly PBLH were observed from 2008 to 2017.

Air pollution is a serious challenge in South Korea and worldwide, and negatively impacts human health and mortality rates. To assess air quality and the spatiotemporal characteristics of atmospheric particulate matter (PM), PM concentrations were compared with meteorological conditions and the concentrations of other airborne pollutants over South Korea from 2015 to 2020, using different linear and non-linear models such as linear regression, generalized additive, and multivariable linear regression models. The results showed that meteorological conditions played a significant role in the formation, transportation, and deposition of air pollutants. PM_2.5 levels peaked in January, while PM₁₀ levels peaked in April. Both were at their lowest levels in July. Further, PM_2.5 was the highest during winter, followed by spring, autumn, and summer, whereas PM₁₀ was the highest in spring followed by winter, autumn, and summer. PM concentrations were negatively correlated with temperature, relative humidity, and precipitation. Wind speed had an inverse relationship with air quality; zonal and vertical wind components were positively and negatively correlated with PM, respectively. Furthermore, CO, black carbon, SO₂, and SO₄ had a positive relationship with PM. The impact of transboundary air pollution on PM concentration in South Korea was also elucidated using air mass trajectories.

The melting layer (ML) is an important region used to describe the transition of hydrometeors from the solid to the liquid phase. It is a typical feature used to characterize the vertical structure of the stratiform precipitation. The present study implements a new automatic melting-layer detection algorithm based on the range-height-indicator–quasi-vertical profile (R-QVP) in the X-band dual-polarization radars. The algorithm uses the gradients of the polarimetric radar variables reflectivity factor at horizontal polarization (Z_h), differential reflectivity (Z_dr), and copolar correlation coefficient (ρ_hv), and their combinations to describe the ML characteristics. The melting layer heights derived from the radar were compared and validated with the heights of the 0 °C wet-bulb temperature derived from the Modern-Era retrospective analysis for research and applications (MERRA) reanalysis datasets and obtained high correlation coefficient 0.96. The R-QVP combined with this algorithm led to spatial and temporal variabilities of the melting layer thickness. The thickness of the melting layer was independent of the seasonal, spatial, and temporal variabilities of the precipitations. Intriguing polarimetric signatures have been observed inside, above, and below the ML, based on the phase of the precipitation particles. The statistics of the polarimetric variables were evaluated for ML, rain, and snow. Further, the linkage between enhanced specific differential phase shift (K_dp) and Z_dr in the dendritic growth layer (DGL) and surface precipitation was also described.

Boundary layer radar (L-band) wind profilers frequently encounter a significant problem arising from the contamination of intermittent clutter, produced by seasonal and nocturnal migrating birds, which often yields erroneous wind velocity and boundary layer information. Classical harmonic wavelet transforms (HWTs) are inadequate in removing the transient clutter contamination under certain conditions, particularly when the clutter is significant. We implemented an adaptive complex harmonic discrete wavelet transform with an advanced statistical method to overcome the shortcomings of the classical wavelet method. This algorithm effectively eliminates the bird contamination even where the classical method fails. Finally, a multiple peak-picking (MPP) algorithm was added to select true atmospheric signals and estimate accurate moments. The obtained wind velocity measurements were compared with those derived using the conventional method and validated with global positioning system radiosonde winds. The comparison shows that the proposed method is more effective than the conventional one.

Doppler velocity inside clouds is measured from space for the first time by Cloud Profiling Radar (CPR) onboard Earth Clouds, Aerosols and Radiation Explorer (EarthCARE). The EarthCARE JAXA Level 2 cloud products are derived from CPR, 355-nm high-spectral-resolution lidar (ATLID) and Multi-Spectral Imager (MSI). The CPR standalone cloud product (CPR_CLP) and the CPR-ATLID synergy product (AC_CLP) were released to the public in March 2025. Version up of these two products as well as CPR-ATLID-MSI synergy product (ACM_CLP) were released in December 2025. These products include cloud mask, height resolved cloud particle types, cloud particle habits, cloud/precipitation microphysics, terminal velocity of cloud and precipitation particles and vertical air motion.This paper presents global analysis of cloud microphysics and vertical velocity by using EarthCARE data. We demonstrate how such information is used to enhance our understanding of cloud formation. Comparisons with existing global products were also conducted. The retrieved cloud and vertical motion were also used to evaluate high resolution numerical models.