Software for Reduced-Dimension Data Assimilation (RDDA)
OHSU # 1654
Software for Reduced-Dimension Data Assimilation (RDDA) uses fast model surrogates to allow rapid data assimilation in complex data sets, including those modeling weather and oceanographic currents. Model surrogates are statistical models that are trained to approximate the dynamics of traditional computational models at a fraction of their computational cost. This method of data assimilation reduces computational run times by several orders of magnitudes.
The RDDA software consists of three components:
1) A dimension reduction toolbox (EOF-toolbox). EOF-toolbox applies Empirical Orthogonal Function (EOF) dimension reduction technique to large spatial-temporal datasets, such as outputs of oceanographic circulation models. Algorithms used in the EOF-toolbox are described in (Frolov 2007).
2) A model surrogate training code (model-surrogate toolbox). Model-surrogate toolbox extends the functionality of an existing neural network training toolbox (Netlab) to training of statistical models with time-lagged inputs and outputs. Algorithms used in the model-surrogate toolbox are described in (Frolov 2007; Frolov et al. 2009; van der Merwe et al. 2007).
3) A set of driver routines that implement the RDDA machinery (RDDA drivers). RDDA drivers plumb together the EOF-toolbox, the model surrogate toolbox, the ReBel toolbox for state estimation, routines for preparation of the observational data specific to the Columbia River observing system, and routines for post processing of model outputs. Estimation algorithms used by the RDDA drivers are described in (Frolov 2007; Frolov et al. 2009).
Frolov S (2007) Enabling technologies for data assimilation in a coastal-margin observatory. Ph.D. thesis, Oregon Health & Science University Portland, OR
Frolov S, Baptista AM, Leen TK, Lu Z, van der Merwe R (2009) Fast data assimilation using a nonlinear Kalman filter and a model surrogate: an application to the Columbia River estuary. Dynamics of Atmosphere and Oceans 48 (1-3):16-45. doi:doi:10.1016/j.dynatmoce.2008.10.004
van der Merwe R, Leen TK, Lu Z, Frolov S, Baptista AM (2007) Fast Neural Network Surrogates for Very High Dimensional Physics-based Models in Computational Oceanography. Neural Networks. doi:doi:10.1016/j.neunet.2007.04.023
- Todd Leen, SM.Biomedical Engineering
- Zhengdong Lu
- Rudolph Van der Merwe
- Sergey Frolov, Coastal Margin Observation & Prediction (CMOP)
- Antonio Baptista, Center for Coastal Margin Observation & Prediction (CMOP)
For more information, contact:
Technology Development Manager