11:00 AM - 1:00 PM
[MGI29-P04] Ensemble-based iterative variational data assimilation approach and its extension for count data
Keywords:data assimilation, ensemble variational method, Gauss-Newton method, Poisson distribution
To avoid the problems with the adjoint model, there exist some ensemble-based approaches for solving the problem of the 4-dimensional variational method without the adjoint model. The ensemble-based approaches employ results of ensemble simulation runs with various initial conditions and parameter settings. These ensemble-based variational methods are much easier to implement than the conventional adjoint method because a simulation model is treated as a black-box. However, since the existing ensemble-based methods are derived under the assumption that observations obey Gaussian distributions, they can not immediately be applied when observations obey other distributions. In this paper, we propose an ensemble-based algorithm for observations obeying Poisson distributions, which can be used for data assimilation into a black-box simulation model. We also conduct a simple experiment with a one-dimensional fluid model to confirm the performance proposed approach.