Forecast models nowadays run routinely at convection-permitting resolution, where individual convective storms are expected to be resolved. However, the evaluation of such models with satellite, ground-based radar networks, and other ground observations reveals that substantial errors remain in the timing and character of these storms.

Various new field campaigns, radar networks, and satellite missions are now aimed at tracking convective storms throughout their life cycle, gathering dynamical and microphysical information at the different stages of storm development.
Long-term object-based analysis of these new observations enhance our understanding of precipitation generating processes, provide valuable information for nowcasting, and allow for models to be tested on storm evolution in novel ways.
In particular, process-oriented approaches to model validation may now be based on multi-storm statistics rather than single case studies.

This session will bring together the remote sensing community, cloud physicists, numerical modelers and forecasters with the aim to advance current understanding of the convective storm life cycles with new statistical observation and modeling approaches.
We welcome contributions on cloud and storm tracking strategies in observations, monitoring and nowcasting of storm development, statistical and climatological analyses of storm life cycles, model sensitivity studies including microphysics and boundary layer parameterization, and forecast verification studies.

Contributions may also focus on individual problems such as storm initiation, embedded convection, mesoscale organization, hail and lightning detection, or factors leading to high-impact localised convective events (including stationary banding). Case studies of single convective events will still be considered, but the goal is to move towards a better general understanding of the evolution of precipitating systems under various conditions, which will lead to improved operational precipitation forecasts.

Solicited presentation by Scott Collis (Argonne National Laboratory, Argonne IL, USA).