## 1 Introduction  
Severe convective weather systems are usually accompanied by short-lived heavy rainfall, thunderstorms, strong winds, tornadoes, and/or hailstorms on the order of a dozen to three hundred kilometers horizontally [1]. The emergence or outbreak of convective weather systems often causes significant economic losses. Turbulence and high-altitude ice formation caused by convective weather systems also seriously threaten the aviation safety [2]. Traditionally, operational numerical weather prediction (NWP) model data is used to predict the occurrence of severe convective weather systems [3]. However, for some isolated and sudden local convective storm systems with short lifetimes, it is still hard to accurately predict their occurrence, development, and movement based on the current NWP models [4]. However, severe convective storm systems can be well tracked or observed by geostationary (GEO) weather satellites and/or ground-based weather radars in their initial stages [5], which are always adopted as convective initiation (CI) products in nowcasting applications. Some previous studies [6; 7] have already pointed out that the GEO orbit weather satellites can well capture sudden convective weather systems with a high spatiotemporal resolution. Normally, based on the GEO satellite observations, features and temporal variations at the cloud top from the infrared (IR) brightness temperature (BT or TBB, temperature of black body) observations are used to track and identify developing convective storm systems [6]. Another significant benefit of the IR-based identification method is the ability to perform a unified and continuous recognition of convective storm systems from day to night, without the need to rely on the reflected sunlight [6; 8]. However, due to remarkable seasonal, regional, or sensor specification differences, there is no unified IR BT threshold for tracking and identifying potential convective cloud clustering [9]. As early as 1980, [PERSON] first used 241 K for IR window band as a criterion to identify mesoscale convective cloud systems (MCS) [10]. In the most recent decade, with the rapid improvement of space-based imaging sensors, [PERSON] et al. [11] found that the presence of high altitude cirrus clouds can significantly impact the accuracy of convective storm system identification. Thus they proposed a new marker of 233 K (BT at 10.5-12.5 \(\upmu\)m band from the European GEO meteorological Satellite-7, and Meteosat-7) for judging convection. Recently, it was found that MCS lifetimes were impacted by the use of a lower IR threshold identification method. [12]. To further improve the accuracy of the single IR band algorithm for CI detection, the BT gradient of IR window band and tropopause temperature from NWP data were used to further analyze convective storm events [13]. Furthermore, [PERSON] [14] found that the water vapor band plays an important role in cloud classification during nighttime hours. When combining the IR window band and the water vapor absorption band, the accuracy of convection classification is higher than that using only one band [14]. In addition to the temporal variation of BT at the top of cloud deck, some BT differences (BTDs) between different spectral bands were also used for detecting convective storm systems [15]. With the vigorous and rapid development of convective systems, a strong updraft will transport water vapor above convective cloud clusters and break through the top of the troposphere into the lower stratosphere [16]. [PERSON] [17] found that when tropospheric water vapor enters the stratosphere, the BTDs at the top of cloud between the water vapor (high BT) and IR window band (low BT) are negative; therefore, he used the BTD between water vapor and IR window band to detect convection systems.