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The method of data assimilation is introduced as a way of combining observational and model information, and take account of their characteristics. It is shown how data assimilation adds value to both observations and model. We then describe examples of data assimilation in various fields, including: (i) chemical data assimilation; (ii) assessment of new observing systems; and (iii) land data assimilation. We then provide an outlook for data assimilation, including recent algorithm developments and new fields where it is being applied.