This study presents the algorithm ACTION, defined as automatic center detection of tropical cyclone (TC) using image processing based on the operational radar network. Based on the high visibility of weather radar imagery, a TC’s motion vector is calculated from the continuous image change using optical flow, producing the TC’s rotation center. The algorithm performance was verified by analyzing the typhoons (TCs in the northwestern Pacific) that affected the Korean Peninsula from 2018–2021, demonstrating a high detection rate of 80.8% within an error distance of 40 km against the best track of the Korea Meteorological Administration (KMA). The detection rate was 100% for typhoons with temporally consistent morphological characteristics. ACTION automatically generates TC center information upon the TC’s initial entry inside the observation radius even in the absence of uniform radar data. As ACTION is capable of performing real-time calculations that are directly applied to rapidly generated weather radar images, it is currently being utilized by the KMA. The high temporal resolution TC center information calculated through ACTION is expected to improve the efficiency of TC forecasting.

Weather radar is a meteorological observation equipment that can detect meso-scale meteorological phenomena as well as small-scale convective phenomena at an early stage with high temporal and spatial resolution. This study intends to introduce radar data used in meteorology into other perspectives. Although the physical meaning of radar data variables is more generally used, actual weather forecasting agencies are more sensitive to visible changes in the radar image itself. Computer vision technology is a representative way to directly utilize radar images. It produces new information by identifying and interpreting objects or situations by imitating the human visual cognitive system. In this study, convective echo changes were detected and tracked by computer vision using high temporal resolution of X-band weather radar images observed at 1-minute intervals. For convective echo detection, the background subtraction method was used, and techniques such as the median value and Gaussian filter were also used to minimize the echo detection error. The paths of the detected echoes were predicted using the Kalman filter technique. In order to classify the detected multiple convective echoes into individual objects, the labeling of individual convective echoes was also implemented by giving a certain size and intensity as conditions. Finding and tracking convective echoes in real-time with high spatio-temporal resolution radar images will help detect signals early that can generate severe weather and respond preemptively to hazardous weather.   Acknowledgements: This research was supportedby the "Development of analysis technologies for local-scale weather radar network and next generation radar (KMA2021-03221)" of "Development of integrated application technology for Korea weather radar" project funded by the Weather Radar Center, Korea Meteorological Administration.

This study presents the algorithm ACTION, defined as Automatic Center detection of Tropical cyclone (TC) using Image processing based on the Operational radar Network. Based on the high visibility of weather radar imagery, the TC’s motion vector is calculated from the continuous image change using optical flow, producing its rotation center as the TC’s center. The algorithm’s performance was verified for typhoons (TCs in the Northwestern Pacific) affecting the Korean Peninsula from 2018–2021 and showed a high detection rate of 80.8% within an error distance of 40 km compared to the best track of the Korea Meteorological Administration (KMA). The detection rate was 100% for typhoons with temporally consistent morphological characteristics. ACTION automatically generates TC center information upon the TC’s initial entry inside the observation radius even in the absence of uniform radar data. ACTION easily calculates using Open Source Computer Vision, performs in real time, and can be directly applied to rapidly generated weather radar images; hence, it is currently being utilized by the KMA. The high-temporal-resolution TC center information calculated through ACTION is expected to improve the efficiency of TC forecasting.

It is very important  to detect hazardous small scale weather phenomena occurred in highly populated areas because of its catastrophic damages. In order to monitor these local events and provide appropriate information about high impact weather, the Weather Radar Center of the Korea Meteorological Administration has installed the X-band radar netwGork consisting of three dual-polarization radars with the solid state power amplifier (SSPA) over the metropolitan area in October 2018. These radars are operating with three elevation angles per 1 minute and 150m gate size.  In this study, we investigated severe weather cases that occurred in the Seoul metropolitan area by the analysis of data from the X-band weather radar observation network. The first case is  a strong convective event that led flash flood and many casualties over the central area of Seoul city within a 6-hour period in August 2022.  The second one is the tornado case that occurred in September 2020. Although it is a rare event on the Korean Peninsula, the high spatial and temporal resolution data of the X-band weather radar observation network can provide detailed characteristics of hazardous weather.  In particular, the X-Band network yielded better results compared with the S-Band weather radar observation network through the high spatio-temporal resolution. In this study, we have found out some benefits of the X-band radar network when we explore the rapid developing small scale convective events. Since the X-band frequency has more attenuation for heavy precipitation and lose signals in wet radom condition, quality control and correction are essential to the performance of the products. Therefore, QC and quantitative estimation of heavy rainfall will be the main objectives of our future study.     Acknowledgements: This research was supported by the "Development of radar based severe weather monitoring technology (KMA2021-03221)" of "Development of analysis technologies for local-scale weather radar network and next generation radar" project funded by the Weather Radar Center, Korea Meteorological Administration.