| Инд. авторы: | Klimova E. |
| Заглавие: | Application of the ensemble Kalman filter to environmental data assimilation |
| Библ. ссылка: | Klimova E. Application of the ensemble Kalman filter to environmental data assimilation // IOP Conference Series: Earth and Environmental Science. - 2018. - Vol.211. - Iss. 1. - Art.012049. - ISSN 1755-1307. - EISSN 1755-1315. |
| Внешние системы: | DOI: 10.1088/1755-1315/211/1/012049; SCOPUS: 2-s2.0-85059596443; |
| Реферат: | eng: 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. © Published under licence by IOP Publishing Ltd. |
| Ключевые слова: | Parameter estimation; Smoothing algorithms; Pollutant emission; Observational data; Numerical experiments; Generalized algorithms; Ensemble Kalman Filter; Data assimilation systems; Advection-diffusion models; Stochastic systems; Kalman filters; Information use; Information systems; Forecasting; Atmospheric chemistry; |
| Издано: | 2018 |
| Физ. характеристика: | 012049 |
| Конференция: | Название: Международная конференция и школа молодых ученых по измерениям, моделированию и информационным системам для изучения окружающей среды Аббревиатура: ENVIROMIS-2018 Город: Томск Страна: Россия Даты проведения: 2018-07-05 - 2018-07-11 |