New technologies for the classification of convective cloud lifecycles and the prediction of their movements are needed to detect severe convective weather and to support objective cloud guidance. Satellites enable earlier detection of severe weather over larger coverage areas than ground-based observations or radar. The use of satellite observations for nowcasting is thus likely. In this study, convective initiation (CI) data are paired with a modified rapid-development thunderstorm (RDT) algorithm for the analysis of new data from the Geostationary Korea Multi-Purpose Satellite-2A (GEO-KOMPSAT-2A, GK2A). The RDT algorithm is further modified to accommodate the additional GK2A satellite channels, and new satellite data are used to continuously analyze thunderstorms associated with severe weather in Korea. The logistic regression (LR) machine learning approach is used to optimize the criteria of interest fields and weighting coefficients of the RDT algorithm for convective detection. In addition, auxiliary data (cloud type, convective rainfall rate, and cloud top temperature/height) calculated from RDT sub-module is replaced with GK2A derived products. The fully modified RDT algorithm (K-RDT) is quantitatively verified using lightning data from summer convection cases. The probability of detection (POD) for convective clouds is increased by 30–40%, and the threat score (TS) for average lightning activity is improved by 10–30%. The channel properties of Japan Himawari-8 satellite are similar to those of the GK2A satellite. Due to the lack of GK2A satellite data during the development period, CI data from the Himawari-8 satellite are used as proxies.

中文翻译：

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用于GK2A卫星的快速发展雷暴（RDT）算法的改进

需要对流云生命周期进行分类和预测其运动的新技术，以检测强对流天气并支持客观的云指导。与地面观测或雷达相比，卫星能够在更大的覆盖范围内更早地检测到恶劣天气。因此有可能将卫星观测用于临近预报。在这项研究中，对流启动（CI）数据与改良的快速发展雷暴（RDT）算法结合使用，用于分析来自对地静止韩国多用途卫星2A（GEO-KOMPSAT-2A，GK2A）的新数据。进一步修改了RDT算法，以容纳更多的GK2A卫星频道，并且使用新的卫星数据来连续分析与韩国恶劣天气相关的雷暴。使用对数回归（LR）机器学习方法来优化对流检测的RDT算法的关注字段和加权系数标准。此外，由RDT子模块计算出的辅助数据（云类型，对流降雨率和云顶温度/高度）将替换为GK2A派生产品。使用夏季对流案例的闪电数据对经过完全修改的RDT算法（K-RDT）进行了定量验证。对流云的探测概率（POD）增加了30–40％，平均闪电活动的威胁评分（TS）提高了10–30％。日本Himawari-8卫星的频道属性与GK2A卫星的相似。由于在开发期间缺少GK2A卫星数据，因此将Himawari-8卫星的CI数据用作代理。