Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Some numerical experiments with variable-storage quasi-Newton algorithms
Mathematical Programming: Series A and B
Recipes for adjoint code construction
ACM Transactions on Mathematical Software (TOMS)
The PALM Project: MPMD Paradigm for an Oceanic Data Assimilation Software
Euro-Par '99 Proceedings of the 5th International Euro-Par Conference on Parallel Processing
Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
| Hi-index | 0.00 |
Variational data assimilation consists in estimating control parameters of a numerical model in order to minimize the misfit between the forecast values and some actual observations. The gradient based minimization methods require the multiplication of the transpose jacobian matrix (adjoint model), which is of huge dimension, with the derivative vector of the cost function at the observation points. We present a method based on a modular graph concept and two algorithms to avoid these expensive multiplications. The first step of the method is a propagation algorithm on the graph that allows computing the output of the numerical model and its linear tangent, the second is a backpropagation on the graph that allows the computation of the adjoint model. The YAO software implements these two steps using appropriate algorithms. We present a brief description of YAO functionalities.