Variability of Convective and Ice Cloud Structures in the RASTA Collection of Kilometer-Scale Meso-NH Simulations for C2OMODO

Measuring vertical velocity is crucial for advancing our understanding of deep convection. The Convective Core Observations through MicrOwave Derivatives in the trOpics (C2OMODO) mission aims to retrieve vertical velocity using ice-sensitive microwave measurements taken at two closely spaced time intervals. In preparation for the mission, it is essential to investigate the relationship between vertical velocity and satellite measurements. However, vertical velocity within convective cores is rarely measured, and no comprehensive dataset currently exists. To address this, it is necessary to create datasets that combine vertical velocity with corresponding synthetic satellite observations. The recent porting of the Meso-NH non-hydrostatic mesoscale model to GPU architectures enables the efficient generation of such high-resolution datasets. We present the RASTA (RAdar SysTem Airborne) collection, a dataset of Meso-NH kilometer-scale simulations developed for the C2OMODO project. It is based on five airborne field campaigns during which the RASTA radar was deployed: CADDIWA, EXAEDRE, HAIC Cayenne, HAIC Darwin and MAESTRO. The dataset comprises 13 billion atmospheric columns from 53 simulations, validated against RASTA radar data and satellite observations. The exploration of the convective cores variability in the dataset is carried out using formal concept analysis (FCA), a mathematical framework that links objects and attributes through a Galois connection. FCA is used to classify convective cores according to the environmental factors that most influence them. The resulting concept lattices reveal which combinations of conditions favor convection. They also highlight common patterns and specific characteristics in different meteorological environments.