Data Assimilation is a modern method to combine information from observational data and numerical prediction model, in order to best define and understand the evolving nature of a physical system. It is currently applied across a wide range of Earth sciences, including weather forecasting, oceanography, atmospheric chemistry, hydrology, and climate studies. This research program is actively involved in the development and application of state-of-the-art data assimilation systems (e.g., ensemble-based and variational schemes) on a wide variety of topics. Research activities include improving weather and air quality forecasts from coupled land-atmosphere-chemistry models, assessing the value of current and future observing systems, and exploring synergies between in-situ, airborne and remotely-sensed observations of the atmosphere and its constituents (e.g. carbon dioxide, carbon monoxide, aerosols, ozone). Data Assimilation serves as one of the critical components of an Earth System numerical laboratory, providing a regionally and globally coherent picture that goes well beyond the snapshots that can be derived from satellite measurements.