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 their
impacts 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 vertical
structure of these systems remains poorly constrained. We use a novel ice cloud dataset called
IceCloudNet to study the temporal evolution of the vertical structure of tropical deep convective
systems 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 the
tropical belt between 30°S–30°N and 30°W–30°E, developed by Jeggle et al. (2025). The spatial
resolution 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 the
Tobac cloud tracking algorithm to the vertically integrated ice water content over the course of the
year 2010, we identify and track deep convective systems to diagnose systematic changes in the
vertical distribution of ice water content during their lifecycle. We also assess the suitability of
IceCloudNet for a robust and physically coherent tracking and analysis of vertically resolved cloud
properties. This allows us to highlight both its limitations and its potential to enable, for the first
time, a comprehensive four-dimensional analysis of the evolution of tropical ice clouds.