New 4D Variational Method for Data Assimilation
Data assimilation (DA) is a computational method that integrates numerical simulation models and observational/experimental data based on Bayesian statistics. DA is not only the main technique in the current weather forecasting, but also applied in the various fields of science. The 4D Variational method (4DVar) is usually used when a given simulation model has a large number of freedom like in the weather forecasting. A shortcoming in the existing 4DVar is that it cannot evaluate an uncertainty of the forecast. For example, circles found on an anticipated course of typhoon, which indicate forecasting errors with respect to the location of the eye of typhoon, are estimated by using an ensemble-based method different from 4DVar.
We have successfully developed a new 4DVar methodology implementing a second-order adjoint method, which enables us to not only obtain a forecast but also evaluate its uncertainty. The new 4DVar is practically capable of such unified estimation (i.e., forecasting and uncertainty quantification) even in the cases of large-scale numerical models, avoiding an ad-hoc combination of 4DVar and other ensemble-based methods.
We expect our new method will be applied in various fields of science. See the following paper in detail.
Ito, S., H. Nagao, A. Yamanaka, Y. Tsukada, T. Koyama, M. Kano, and J. Inoue
Data assimilation for massive autonomous systems based on a second-order adjoint method
Phys. Rev. E, 94, 043307, doi:10.1103/PhysRevE.94.043307, 2016.