The 8th WMO Symposium on Data Assimilation & 12th International Symposium on Data Assimilation

Data assimilation across scales and disciplines 

Data assimilation (DA) is an interdisciplinary science that integrates model simulations with real-world observations in a harmonious way, leveraging statistical mathematics, dynamical systems theory, and more recently, machine learning (ML). DA has long been a cornerstone of numerical weather prediction. DA has rapidly evolved into a unifying scientific methodology that spans multiple scales — from short-term weather forecasting to long-term paleoclimate reconstruction — and diverse disciplines, including data science, geoscience, biology, and engineering. Recent advances in observation, computation, and ML are transforming the landscape of DA. With the growing integration of ML, DA techniques are being refined to improve predictive capabilities and enhance data-driven modeling, and combined with system control theory, create a powerful framework for improving system performance by integrating real-time observations to refine models and control strategies.