Storm and convective-scale weather data analysis and prediction still present significant challenges for atmospheric sciences. Addressing these challenges requires a synergy of advances in high-resolution observations, modeling, and data assimilation.
This session invites contributions from developments in
• Convective-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
• Ensembles and uncertainty quantification using machine learning
• Advances in convective-scale modeling and parameter estimation
• Assimilation of ground and space-based radar data
• Active and passive satellite data assimilation
• Assessment of the impact of convective-scale data assimilation on global and regional prediction
• Observation operators for remote sensing and data assimilation
• Observations at convective scales: new observing technologies and strategies
This session invites contributions from developments in
• Convective-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
• Ensembles and uncertainty quantification using machine learning
• Advances in convective-scale modeling and parameter estimation
• Assimilation of ground and space-based radar data
• Active and passive satellite data assimilation
• Assessment of the impact of convective-scale data assimilation on global and regional prediction
• Observation operators for remote sensing and data assimilation
• Observations at convective scales: new observing technologies and strategies