The field of data assimilation and predictive control is witnessing significant advancements, driven by the development of innovative algorithms and models. Researchers are focusing on improving the accuracy and efficiency of data assimilation techniques, such as modular and hierarchical approaches, to enhance predictability and reduce computational complexity. Additionally, there is a growing interest in integrating data-driven methods with model-based techniques to leverage the strengths of both paradigms. Noteworthy papers in this area include:

• Modular data assimilation for flow prediction, which presents a 2-step algorithm that overcomes many algorithmic inadequacies of standard nudging methods.
• Elucidated Rolling Diffusion Models for Probabilistic Weather Forecasting, which introduces a framework that successfully unifies a rolling forecast structure with high-fidelity diffusion techniques. These developments have the potential to revolutionize various applications, including climate modeling, weather forecasting, and control systems, by providing more accurate and robust predictions and improving decision-making capabilities.