Application of the ensemble Kalman filter to environmental data assimilation

International Conference and Early Career Scientists School on Environmental Observations, Modeling and Information Systems: ENVIROMIS-2018

生态环境科学;计算机科学

Klimova, Ekaterina^1

Institute of Computational Technologies SB RAS, Prosp. Akad. Lavrentyeva, 6, Novosibirsk

630090, Russia^1

关键词: Advection-diffusion models;    Data assimilation systems;    Ensemble Kalman Filter;    Generalized algorithms;    Numerical experiments;    Observational data;    Pollutant emission;    Smoothing algorithms;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/211/1/012049/pdf
DOI  :  10.1088/1755-1315/211/1/012049

来源: IOP

【 摘 要 】

Assessment of the state of the environment with observational data is one of the most urgent modern issues. Such an assessment can be made using forecast models based on data assimilation systems. One of the most popular algorithms for data assimilation is the ensemble Kalman filter, in which the forecast error covariance is estimated using an ensemble of forecasts for perturbed initial fields. Parameter estimation is an important part of atmospheric chemistry modelling. In particular, pollutant emission may be a parameter to be estimated. A single-time estimation based on observations may not give the required accuracy. In this context, the method of ensemble smoothing (EnKS), which uses data from the entire time interval to estimate the parameter at a given time, is becoming increasingly popular. In this paper, we consider a generalization of a previously proposed method called the ensemble π-algorithm, which is a variant of stochastic ensemble Kalman filter. The generalized algorithm is an ensemble smoothing algorithm in which ensemble smoothing is performed for the sample average value and then the ensemble of perturbations is transformed. The proposed algorithm is stochastic. Numerical experiments with a 1-dimensional advection-diffusion model are carried out with the smoothing algorithm proposed in the article.