The characteristics of microphysical processes of a severe winter storm that occurred on the Korean Peninsula on 12 December 2013 was studied in this work for the first time via X-band dual-polarization weather radar observations. A new range–height indicator (RHI) scan-based quasi-vertical profile methodology, in which polarimetric radar variables were averaged at each height of the RHI scan, was introduced to investigate the snow microphysics, and the obtained polarimetric radar signatures served as fingerprints of the dendritic growth, aggregation, and riming processes. Enhanced differential reflectivity (Zdr) and specific differential phase shift (Kdp) bands were detected near the −15°C isotherm, which signified the growth of dendrites or platelike crystals. The observed correlation between the increases in the reflectivity factor at horizontal polarization Zh and copolar correlation coefficient ρhv and the decreases in Zdr and Kdp magnitudes at lower heights suggested the occurrence of the aggregation process. The combination of high Zh and low Zdr values with turbulent atmospheric conditions observed at the ground level indicated the occurrence of the riming process. In addition, the negative Kdp and Zdr values combined with high Zh and ρhv magnitudes (observed near the end of the snow event) indicated the formation of graupel particles. The polarimetric radar signatures obtained for the snow growth processes were evident from ground observations and agreed well with the results of the Weather Research and Forecasting Model and Modern-Era Retrospective Analysis for Research and Applications data. Furthermore, the spatial variability of Zh methodology was implemented to describe both aggregates and rimed ice particles.

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.

Various non-atmospheric signals contaminate radar wind profiler (RWP) data, which produce bias in estimation of moments and wind velocity. Especially, in ultra high frequency (UHF) RWPs, ground clutter severely degrades wind velocity estimation. Furthermore, at higher altitudes, noise dominates the clear air signal. Thus, the important tasks of signal processing in a RWP are (i) to eliminate the clutter signal, (ii) to detect the weak atmospheric signals buried inside the noise and (iii) to improve signal-to-noise ratio. Wavelet analysis is a powerful tool to differentiate the characteristics of the ground clutter and noise from the atmospheric turbulence echo at the time series level. The authors have implemented the signal processing for lower atmospheric wind profiler radar at National Atmospheric Research Laboratory, Gadanki, India, using wavelet transforms. In this study, they present the implementation approach and results. The wavelet-based algorithms use different threshold levels to identify and remove ground clutter and to denoise the data. The obtained results using this method are validated with collocated global positioning system radiosonde data.

Various nonatmospheric signals contaminate radar wind profiler data, introducing bias into the moments and wind velocity estimation. This study applies a fuzzy logic-based method to Doppler velocity spectra to identify and eliminate the clutter echoes. This method uses mathematical analyses and a fuzzy inference system applied to each Doppler velocity spectrum to separate the atmospheric signals from the clutter. After eliminating the clutter, an adaptive algorithm is used to estimate mean Doppler velocities accurately. This combination of techniques is applied to the spectral data obtained by the newly developed 53-MHz active phased array radar located at the National Atmospheric Research Laboratory (NARL), Gadanki, India (13.58N, 798E). Winds derived using the conventional method and the method developed for this study are compared with those obtained by collocated GPS radiosonde. The comparison shows that the present method derives the winds more accurately compared to the conventional method.

This paper presents the efficacy of a “tuned” fuzzy logic method at determining the height of the boundary layer using the measurements from a 1280 MHz lower atmospheric radar wind profiler located in Gadanki (13.5°N, 79°E, 375 mean sea level), India, and discusses the diurnal and seasonal variations of the measured convective boundary layer over this tropical station. The original fuzzy logic (FL) method estimates the height of the atmospheric boundary layer combining the information from the range‐corrected signal‐to‐noise ratio, the Doppler spectral width of the vertical velocity, and the vertical velocity itself, measured by the radar, through a series of thresholds and rules, which did not prove to be optimal for our radar system and geographical location. For this reason the algorithm was tuned to perform better on our data set. Atmospheric boundary layer heights obtained by this tuned FL method, the original FL method, and by a “standard method” (that only uses the information from the range‐corrected signal‐to‐noise ratio) are compared with those obtained from potential temperature profiles measured by collocated Global Positioning System Radio Sonde during years 2011 and 2013. The comparison shows that the tuned FL method is more accurate than the other methods. Maximum convective boundary layer heights are observed between ~14:00 and ~15:00 local time (LT = UTC + 5:30) for clear‐sky days. These daily maxima are found to be lower during winter and postmonsoon seasons and higher during premonsoon and monsoon seasons, due to net surface radiation and convective processes over this region being more intense during premonsoon and monsoon seasons and less intense in winter and postmonsoon seasons.