This session will focus on recent advances in data assimilation and ensemble forecasting across a wide range of temporal scales, from short-range forecast to subseasonal and seasonal prediction. Emphasis will be placed on the links between data assimilation strategies and the ability of ensemble prediction systems to produce skillful, reliable, and actionable forecasts, particularly for high-impact and extreme weather events.
We welcome contributions addressing both traditional and machine learning–based approaches to data assimilation, ensemble generation, and ensemble utilization. Of particular interest are studies that explore how these techniques evolve with forecast lead time, including the transition from short-range to medium-range, extended-range, and seasonal forecasting systems. Contributions highlighting the perspective of operational forecasters and the practical use of ensembles in forecasting and decision-making, especially for extreme events, are strongly encouraged.
The conveners invite papers on a broad range of topics related to Data Assimilation and Ensemble Forecasting for weather and climate prediction, including (but not limited to):
- intercomparison and assessment of the complementarity between different data assimilation techniques, such as Kalman filtering, variational methods, hybrid approaches, and nudging techniques for frequent or rapid update analysis cycles;
- variational data assimilation with extended assimilation windows, including weak-constraint formulations that allow for the explicit representation of model error;
- ensemble data assimilation systems and flow-dependent estimation of background, observation, and model error statistics;
- representation of uncertainties in initial conditions, model physics, boundary conditions, and coupling strategies in global and limited-area ensemble prediction systems, across time scales from short-range to seasonal;
- strategies for bridging weather and climate prediction, including ensemble methods tailored for subseasonal to seasonal forecasting;
- verification, calibration, and post-processing methods for ensemble prediction systems, with attention to scale-dependent performance;
- use of multi-model and ensemble databases;
- applications of ensemble forecasts across different sectors, including energy, health, transport, agriculture, insurance, finance, and climate-sensitive decision support.