Data assimilation (DA) is a powerful tool for combining the information contained in observations with models. The output can be a more accurate estimate of the earth system state than can be provided by either the observations or model alone.
Most data assimilation methods can be derived from Bayes’ theorem but crucial differences exist in the algorithms:
The best method to use depends on the intended application, as the validity of assumptions and the data available differ substantially between e.g. monitoring changes to the land surface, numerical weather prediction and forecasting the ocean ecosystem.
The tools provided here include EMPIRE which allows for any model to be used with ensemble data assimilation techniques such as the EnKF and the particle filter. Also included are application-specific DA tools such as those for the ocean (NEMOVar) and for the land surface (EOLDAS and CARDAMON).
Contact: Jeff Exbrayat (University of Edinburgh).
Contact: Keith Haines (University of Reading)
Contact: Peter Jan (University of Reading)
Contact: Stefano Ciavatta (Plymouth Marine Laboratory)