Introduction_to_dataassimilation
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This document provides an introduction to data assimilation and the OpenDA framework. It discusses how data assimilation combines models and observations to improve model predictions while accounting for uncertainties. Several examples of data assimilation applications are provided, such as for storm surge forecasting and calibrating wave models. The motivation for developing the OpenDA framework is explained, as it aims to provide reusable data assimilation algorithms and components that can be easily implemented and tested across different models.
Introduction_to_dataassimilation
- 1. Introduction to data assimilation Nils van Velzen May 2016 1
- 2. Outline • Data Assimilation in a nutshell • Models and uncertainty • Many examples • OpenDA
- 3. Data assimilation and model calibration • (real time) data assimilation • model calibration • impact of observations • reconstruction of sources
- 4. Models in OpenDA • 𝑥 𝑡 + ∆𝑡 = 𝑀 𝑥 𝑡 , 𝑢 𝑡 , 𝑝, 𝑤 𝑡 • State • Parameters • Forcing • Stochastic term
- 5. Models in OpenDA • Models in Data Assimilation context are stochastic! • Normal dynamic model (codes) are not
- 13. Example of data assimilation Karup catchment
- 14. Karup point A
- 15. Karup point B
- 16. Example of data assimilation Operational storm surge forecasting
- 17. Example of data assimilation Operational storm surge forecasting
- 18. Example of model calibration totS = wind input + non-linear interactions (quadruplets & triads) + whitecapping + bottom friction + depth induced wave breaking SWAN 3rd generation wave model Wave breaking and interaction over a bar
- 19. Example of model calibration SWAN 3rd generation wave model
- 20. Example of model calibration SWAN 3rd generation wave model
- 21. Motivation for OpenDA • The algorithms are model/observation independent BUT (!) • DA methods work on top of models → serious changes to model code • Spaghetti code • Difficult to change algorithm • Maintenance problems • Difficult to test algorithms • Difficult/impossible to reuse code • Expensive!
- 22. OpenDA Content: •Set of interfaces that define interactions between components •Library of data-assimilation algorithms •DA philosophy •Building blocks only need to be implemented once
- 23. OpenDA • Open source (LGPL) • Why OpenDA? • More efficient than development for each application • Shared knowledge between applications • Development of algorithms with e.g. universities • Easier to test, which should result in fewer bugs • Optimized building blocks • Development template
- 24. OpenDAAssociation • Open association for developers development of OpenDA • Ensure well-being of OpenDA
- 25. • Generic semi parallel due to OO concepts EnKF semi parallel
- 26. EnKF semi parallel Lotos-euros air quality model
- 27. Lessons learned • Data assimilation combine models with observations • Modelling of uncertainties • Improve your model predictions • Think about what to expect • Optimization is an art • OpenDA