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Practical Ensemble Data Assimilation with the Parallel Data Assimilation Framework

Data assimilation combines observational data with a numerical model. It is commonly used in numerical weather prediction, but is also applied in oceanography, hydrology and other areas of Earth system science. By integrating observations with models in a quantitative way, data assimilation allows to estimate model states with reduced uncertainty, e.g. to initialize model forecasts. Also, it can estimate parameters that control processes in the model or fluxes. Hence, it can provide information about non-observable quantities if the model represents those. The combination of modeled and observed data requires error estimates for both sources of information. In ensemble data assimilation the error in the model state is estimated by an ensemble of model state realizations. This uncertainty estimate is then used by the assimilation method like the ensemble Kalman filter or a particle filter.

To simplify the implementation and use of ensemble data assimilation, the Parallel Data Assimilation Framework (PDAF) has been developed. PDAF is a freely available open-source software (http://pdaf.awi.de) that provides ensemble-based data assimilation methods like the ensemble Kalman filter, but also allows to perform pure ensemble simulations. PDAF can be used from small toy problems running on notebook computers up to high-dimensional Earth system models running on supercomputers.

The course will, after a short introduction to the ensemble data assimilation methodology, provide a hands-on and interactive example of building a data assimilation system based on a simple numerical model. This practical introduction will prepare the participants to build a data assimilation system for their numerical models with PDAF and hence provide a quick start for applying ensemble assimilation to their individual problems.

This course is both for the novices as well as for data-assimilation experts. It will be useful for novices who have a modelling application and observations and are interested in applying data assimilation, but haven't found a starting point yet. Data-assimilation experts who want to enhance the performance of their applications, or are keen to accelerate development of new data-assimilation methods and new applications will also benefit from the course.

For the interactive hands-on example we will provide source code for download at http://pdaf.awi.de/EGU2021 from April 19.

Co-organized by HS11
Convener: Lars Nerger | Co-conveners: Wolfgang Kurtz, Nabir MamnunECSECS, Qi TangECSECS, Gernot Geppert

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