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Accurate tracking of all components (including core, anvil, and cirrus) of deep convective systems (DCSs) throughout their lifecycle is key to quantifying their impacts on radiative forcing, especially of the anvil and cirrus. Here, a new Full‐tracking Algorithm for Convective Thunderstorm System is developed based on geostationary satellite. It successfully tracks DCSs starting from the initial core to complete dissipation of cirrus detrained from them, and integrates all the related components that split from the initial convective core into a whole DCS. Results show that more than half of the tracked DCSs experience splitting evolutions, with an average of eight sub‐cores during their lifetime. With tracking cirrus generated by DCSs, the lifetime of DCSs is lengthened by up to 10 hr, and their area is enlarged by 16% on average. Generally, long‐lived DCSs have lower cloud top temperature, greater rainfall, and larger area, with more frequent splitting evolutions than short‐lived DCSs. Additionally, DCSs always reach their peaks within 6 hr after initiation regardless of their lifetime. This paper provides a basis for further quantifying the evolution of DCS properties, their impacts on the global radiation budget, and the water cycle in the climate system. Key Points: A novel method is developed to track full components of deep convective systems (DCSs) from their initiation to total cirrus dissipationLifetime of DCSs is extended by up to 10 hr, and their area is enlarged by 16% averagely after continuously tracking their detrained cirrusIntegrated total rainfall amounts are comparably contributed between frequent but small and rare but large DCSs [ABSTRACT FROM AUTHOR]

The current Geostationary Operational Environmental Satellites (GOES-16 and 17) cloud-top phase classification algorithm is based primarily on empirical thresholds at multiple wavelengths that have varying absorption capabilities for water and ice. The performance of current GOES-16 cloud-top phase product largely depends on the accuracy of the selection of reflectance ratios. This study aims at presenting a novel cloud-top phase classification algorithm (the Multi-channel Imager Algorithm, MIA) that provides a more judicious selection of relationships between channels using a supervised K-mean clustering method on multi-channel Red-Green-Blue images. The K-mean clustering method works analogously to how human eyes separate different colors in a microphysical color rendering set of satellite images, which differentiates water, ice and unclassified thin clouds. For water phase, cloud-top temperature information is used to further distinguish supercooled water. To evaluate the performance of the MIA, an extensive comparison with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), Moderate Resolution Imaging Spectroradiometer, and current GOES-16 cloud-top phase products is conducted, using CALIOP as the benchmark. Compared to the current GOES-16 cloud-top phase product, MIA demonstrates a substantial improvement in phase classification, where hit rate increases from 69% to 76% over the Continental United States and 58% to 66% over the full disk domain. • Proposed a geostationary satellite cloud top phase classification algorithm (MIA). • Cloud types classified by MIA include ice, supercooled water, and warm liquid water. • Performance decreases in multi-layer cloud scenario by using CALIOP as benchmark. • Overall hit rate of MIA outperforms current GOES-16 cloud top phase product. [ABSTRACT FROM AUTHOR]