The cloud measurements for two years from the vertical pointing Ka-band cloud radar at Boseong in Korea are used to analyze detailed cloud properties. The reflectivity of the cloud radar is calibrated with other vertical pointing radars compared with the two disdrometers. A simple threshold-based quality control method is applied to eliminate non-meteorological echoes (insects and noise) in conjunction with despeckling along the radial direction. Clouds are classified into five types: high (HC), middle (MC), low (LC) for non-precipitating clouds, and deep (RainDP) and shallow (RainSH) for precipitating clouds. The average cloud frequency was about 35.9% with the maximum frequency of 50% in June for the total two-year sampling period. The RainDP occurred most frequently (11.8%), followed by HC (9.3%), MC (7.4%), RainSH (4.4%), and LC (2.9%) out of the average occurrence of the total 35.9%. HC and RainDP were frequently observed in summer and autumn, while RainSH, LC, and MC were dominant in the winter due to the dominant cloud development by the air-sea interaction during the cold air outbreak. The HC showed a significant seasonal variation of the maximum height and the rapid growth in the layer above 7 km (about −15 °C) in summer and autumn. This rapid growth appears in HC, MC, LC, and RainDP and is linked with rapid increases in Doppler velocity and mass flux. Thus, this growth is originated from the dominant riming processes in addition to depositional growth and is supported by an updraft in the layer between 6 and 8 km. MC showed a single frequency peak around 6 km with rapid growth above and strong evaporation below. The Doppler velocity of MC rapidly increases above 8 km and is nearly constant below this height due to strong evaporation except in the summer. LC had a similar trend of reflectivity (rapid growth in the HC region and strong evaporation in the lower region) lacking high frequency in the MC region. Unlike LC, the RainDP had continuous growth toward the ground in the entire layer with rapid growth in the HC and MC regions. In addition, two modes (cloud and precipitation) appear on the ground in spring and fall with the vertical continuity of the high frequency in the precipitation mode. The precipitation growth was most efficient in RainSH in summer with a reflectivity gradient of about 20 dBZ km⁻¹ and frequent updrafts larger than 1 m s⁻¹ and was smaller in the MC and HC regions.

Estimating precipitation area is important for weather forecasting as well as real-time application. This paper aims to develop an analytical framework for efficient precipitation area estimation using S-band dual-polarization radar measurements. Several types of factors, such as types of sensors, thresholds, and models, are considered and compared to form a data set. After building the appropriate data set, this paper yields a rigorous comparison of classification methods in statistical (logistic regression and linear discriminant analysis) and machine learning (decision tree, support vector machine, and random forest). To achieve better performance, spatial classification is considered by incorporating latitude and longitude of observation location into classification, compared with non-spatial classification. The data used in this study were collected by rain detector and present weather sensor in a network of automated weather systems (AWS), and an S-band dual-polarimetric weather radar during ten different rainfall events of varying lengths. The mean squared prediction error (MSPE) from leave-one-out cross validation (LOOCV) is computed to assess the performance of the methods. Of the methods, the decision tree and random forest methods result in the lowest MSPE, and spatial classification outperforms non-spatial classification. Particularly, machine-learning-based spatial classification methods accurately estimate the precipitation area in the northern areas of the study region.

Aircraft icing refers to the accumulation of ice on the surface and components of an aircraft when supercooled water droplets collide with the aircraft above freezing levels (at altitudes at which the temperature is below 0 °C), which requires vigilant monitoring to avert aviation accidents attributable to icing. In response to this imperative, the Weather Radar Center (WRC) of the Korea Meteorological Administration (KMA) has developed a real-time icing detection algorithm. We utilized 3D dual-polarimetric radar variables, 3D atmospheric variables, and aircraft icing data and statistically analyzed these variables within the icing areas determined by aircraft icing data from 2018–2022. An algorithm capable of detecting icing potential areas (icing potential) was formulated by applying these characteristics. Employing this detection algorithm enabled the classification of icing potential into three stages: precipitation, icing caution, and icing warning. The algorithm was validated, demonstrating a notable performance with a probability of detection value of 0.88. The algorithm was applied to three distinct icing cases under varying environmental conditions—frontal, stratiform, and cumuliform clouds—thereby offering real-time observable icing potential across the entire Korean Peninsula.

The vertical structure of ice clouds and vertical air motion (V_air) were investigated using vertically pointing Ka-band cloud radar. The distributions of reflectivity (Z), Doppler velocity (V_D), and spectrum width (SW) were analyzed for three ice cloud types, namely, cirrus, anvil, and stratiform clouds. The radar parameters of the cirrus clouds showed narrower distributions than those of the stratiform and anvil clouds. In the vertical structures, the rapid growth of Z and V_D occurred in the layer between 8 and 12 km (roughly a layer of −40 °C to −20 °C) for all ice clouds. The prominent feature in the stratiform clouds was an elongated “S” shape in the V_D near 7–7.5 km (at approximately −16 °C to −13 °C) due to a significant decrease in an absolute value of V_D. The mean terminal fall velocity (V_t) and V_air in the ice clouds were estimated using pre-determined V_t–Z relationships (V_t = aZ^b) and the observed V_D. Although the cirrus clouds demonstrated wide distributions in coefficients a and exponents b depending on cloud heights, they showed a smaller change in Z and V_t values compared to that of the other cloud types. The anvil clouds had a larger exponent than that of the stratiform clouds, indicating that the ice particle density of anvil clouds increases at a faster rate compared with the density of stratiform clouds for the same Z increment. The significant positive V_air appeared at the top of all ice clouds in range up to 0.5 m s⁻¹, and the anvil clouds showed the deepest layer of upward motion. The stratiform and anvil clouds showed a dramatic increase in vertical air motion in the layer of 6–8 km as shown by the rapid decrease of V_D. This likely caused increase of supersaturation above. A periodic positive V_air linked with a significant reduction in V_D appeared at the height of 7–8 km (approximately −15 °C) dominantly in the stratiform clouds. This layer exhibited a bi-modal power spectrum produced by pre-existing larger ice particles and newly formed numerous smaller ice particles. This result raised a question on the origins of smaller ice particles such as new nucleation due to increased supersaturation by upward motion below or the seeder-feeder effect. In addition, the retrieved V_air with high-resolution data well represented a Kelvin-Helmholtz wave development.

Aircraft icing, a hazardous phenomenon involving the accumulation of ice or supercooled water droplets on an aircraft’s wings and airframe in freezing temperatures when an airplane flies through clouds, poses substantial risks to aviation safety. From a meteorological perspective, aircraft icing is determined by the air temperature, and the number and size of water droplets. In particular high values with water contents are a crucial factor correlating with increased icing intensity. Since weather radar can detect ice and water droplets larger than 2 mm in diameter within precipitable clouds, we have developed two radar-based aircraft icing products (icing potential areas and icing intensity) to facilitate safe aviation services in real-time. In this study, the estimation algorithm of aircraft icing intensity using Z-LWC relationship was presented. We utilized data from a ground-based S-band radar mosaic, an icing detector, and a cloud droplet probe installed on the aircraft (KMA/NIMS atmospheric research aircraft; NARA) for 13 cases of icing. Within the icing areas determined by the icing detector, 3-dimensional gridded reflectivity (Z) and liquid water content (LWC) were matched based on time and location. A Z-LWC relationship was then derived using the paired dataset sorted by size. We calculated LWC from Z using this relationship and categorized icing intensity into Trace, Light, Moderate, Heavy, and Severe, according to FAA criteria (FAA 2001). The estimation of icing intensity was solely focused within the identified icing potential areas (Kim et al. 2023). The algorithm was validated using a “Light” intensity icing case from aircraft report (AIREP), showing good performance, but further verification is needed. ACKNOWLEDGEMENTSThis research was supported by “Development of integrated radar analysis and customized radar technology (KMA2021-03021)” of “Development of integrated application technology for Korea weather radar” project funded by the Weather Radar Center, Korea Meteorological Administration.This work was funded by the Korea Meteorological Administration Research and Development Program "Developing Application Technology using Atmospheric Research Aircraft" under Grants (KMA2018-00222).