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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. [ABSTRACT FROM AUTHOR]

This study presents an easy and convenient empirical method to optimize polarimetric variables and produce more accurate dual polarization radar rainfall estimation. Weather Radar Center (WRC) in Korea Meteorological Administration (KMA) suggested relations between polarimetric variables (Z-ZDR and Z-KDP) based on a 2-D Video Distrometer (2DVD) measurements in 2014. Observed polarimetric variables from CAPPI (Constant Altitude Plan Position Indicator) images composed at 1 km of height were adjusted using the WRC's relations. Then dual polarization radar rainfalls were estimated by six different radar rainfall estimation algorithms, which are using either Z, Z and ZDR, or Z, ZDR and KDP. Accuracy of radar rainfall estimations derived by the six algorithms using the adjusted variables was assessed through comparison with raingauge observations. As a result, the accuracy of the radar rainfall estimation using adjusted polarimetric variables has improved from 50% to 70% approximately. Three high rainfall events with more than 40 mm of maximum hourly rainfall were shown the best accuracy on the rainfall estimation derived by using Z, ZDR and KDP. Meanwhile stratiform event was gained better radar rainfalls estimated by algorithms using Z and ZDR. [ABSTRACT FROM AUTHOR]

Non-precipitation echoes due to ground and sea clutter, chaff, anomalous propagation, biological targets, and interference in weather radar observations are major issues causing a decline in the accuracy of meteorological and hydrological applications based on radar data. Statistically based quality control techniques using polarimetric variables have improved the accuracy of radar echo classification, however their performance is affected by attenuation, nonuniform beam filling, and hydrometeor diversity as well as terrain blockage, beam broadening, and noise correction issues due to the quality degradation of polarimetric measurements. To address this, a new quality control algorithm, named clutter elimination algorithm for non-precipitation echo of radar data (CLEANER), was designed by employing independent feature parameters and variable classification conditions with spatial and temporal observation environments to adapt to these meteorological artifacts and observational limitations. CLEANER was applied to several precipitation cases with various non-precipitation echoes, showing improved performance compared with results from the fuzzy logic-based quality control algorithm in terms of non-precipitation echo removal as well as in precipitation echo conservation. In addition, CLEANER shows better computational efficiency and robustness, as well as an excellent expandability for different radar networks. [ABSTRACT FROM AUTHOR]