Improvement of the Rapid-Development Thunderstorm (RDT) Algorithm for Use with the GK2A Satellite

Abstract

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.