Data assimilation comprises steps to improve the level of interpretability of the input data, by enrichment (get rid of spatial/temporal gaps), by accounting for heterogeneity (through harmonization), and by integration (combination with other data that is relevant to the application). Thereby, datasets become more comparable to each other.