Storm and convective-scale weather data analysis and prediction still present significant challenges for the atmospheric sciences. Making progress requires synergy of advances in high spatial and temporal resolution observations, storm and convective-scale data assimilation methodology, machine learning and ensemble modeling. This session intent is to provide a platform for exchange on the state-of-the-art developments, ongoing progress and ideas for future directions in all aspects of this multifaceted challenge

This session invites contributions from developments in

• Storm and convective-scale data assimilation techniques
• Multi-scale data assimilation techniques
• Use of machine learning in convective-scale data assimilation
• Applications of machine learning to forecasting on convective scales
• Convective-scale model and observation uncertainty representation
• Ensemble design and uncertainty quantification at storm and convective scales using machine learning
• Convective-scale modeling and parameter estimation
• Assimilation of ground and space-based radar data
• Assimilation of geostationary satellite observations
• All-sky satellite data assimilation at storm and convective scales
• Observation operators for remote sensing
• Assessment of the impact of convective-scale data assimilation on weather prediction
• Observations at convective scales: new observing technologies and strategies