Temporal evolution of the vertical structure of tropical deep convective systems in IceCloudNet

Understanding the vertical structure of deep convective systems is essential for assessing theirimpacts on the atmospheric energy budget and hydrological cycle and for evaluating their repre-sentation in models. However, because of limitations of current satellite observations, the verticalstructure of these systems remains poorly constrained. We use a novel ice cloud dataset calledIceCloudNet to study the temporal evolution of the vertical structure of tropical deep convectivesystems on the basis of ice water content as a marker for convective intensity and anvil devel-opment. IceCloudNet is the first 4D-consistent semi-observational ice cloud dataset covering thetropical belt between 30°S–30°N and 30°W–30°E, developed by Jeggle et al. (2025). The spatialresolution is 3 km in the horizontal and 240 m in the vertical. The temporal resolution is 15 min.The dataset is constructed by filling observational gaps using machine learning. By applying theTobac cloud tracking algorithm to the vertically integrated ice water content over the course of theyear 2010, we identify and track deep convective systems to diagnose systematic changes in thevertical distribution of ice water content during their lifecycle. We also assess the suitability ofIceCloudNet for a robust and physically coherent tracking and analysis of vertically resolved cloudproperties. This allows us to highlight both its limitations and its potential to enable, for the firsttime, a comprehensive four-dimensional analysis of the evolution of tropical ice clouds.