Authors: Marc E. Ridler, Nils van Velzen, Stef Hummel, Inge Sandholt, Anne Katrine Falk, Arnold Heemink, Henrik Madsena
Data assimilation optimally merges model forecasts with observations taking into account both model and observational uncertainty. This paper presents a new data assimilation framework that enables the many Open Model Interface (OpenMI) 2.0 .NET compliant hydrological models already available, access to a robust data assimilation library. OpenMI is an open standard that allows models to exchange data during runtime, thus transforming a complex numerical model to a ‘plug and play’ like component. OpenDA is an open interface standard for a set of tools, filters, and numerical techniques to quickly implement data assimilation. The OpenDA–OpenMI framework is presented and tested on a synthetic case that highlights the potential of this new framework. MIKE SHE, a distributed and integrated hydrological model is used to assimilate hydraulic head in a catchment in Denmark. The simulated head over the entire domain were significantly improved by using an ensemble based Kalman filter.
OpenDA; OpenMI; Data assimilation; Hydrological modeling; Kalman filter; Uncertainty