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 precipitation nowcasting methods have been elaborated over the centuries because rain has a crucial impact on human life. Not only quantitative precipitation forecast (QPF) models and convolutional long short-term memory (ConvLSTM), but also various sophisticated methods such as the latest MetNet-2 are emerging. In this paper, we propose a paired complementary temporal cycle-consistent adversarial networks (PCT-CycleGAN) for radar-based precipitation nowcasting, inspired by cycle-consistent adversarial networks (CycleGAN), which shows strong performance in image-to-image translation. PCT-CycleGAN generates temporal causality using two generator networks with forward and backward temporal dynamics in paired complementary cycles. Each generator network learns a huge number of one-to-one mappings about time-dependent radar-based precipitation data to approximate a mapping function representing the temporal dynamics in each direction. To create robust temporal causality between paired complementary cycles, novel connection loss is proposed. And torrential loss to cover exceptional heavy rain events is also proposed. The generator network learning forward temporal dynamics in PCT-CycleGAN generates radar-based precipitation data 10 minutes from the current time. Also, it provides a reliable prediction of up to 2 hours with iterative forecasting. The superiority of PCT-CycleGAN is demonstrated through qualitative and quantitative comparisons with several previous methods.

This paper is submitted to accompany the article “Analyses of three-dimensional weather radar data from volcanic eruption clouds” [1]; it describes three-dimensional (3D) visualizations of the Sakurajima volcanic eruption clouds and the weather radar data used for analyses, as well as their availability and downloading procedures. The radar data were acquired by an operational X-band weather radar located approximately 11 km south of the Showa vent of Sakurajima in Kagoshima, Japan. The original raw radar data are available from the “XRAIN Precipitation Original Data search and Download System”, which is hosted on the website “Data Integration and Analysis System (DIAS)”. Animated images of the radar data shown here, which provide a visual explanation of the temporal evolution and the inner structure of volcanic eruption clouds, were created using the program “Analysis Tools of Three-dimensional Weather Radar Data (AN3D)”. The detailed methods of ANT3D are provided in the co-submitted article “Construction of three-dimensional weather radar data from volcanic eruption clouds” [2].