| Инд. авторы: | Klimova E.G. |
| Заглавие: | Bayesian approach to data assimilation based on ensembles of forecasts and observations |
| Библ. ссылка: | Klimova E.G. Bayesian approach to data assimilation based on ensembles of forecasts and observations // IOP Conference Series: Earth and Environmental Science. - 2019. - Vol.386. - Iss. 1. - Art.012038. - ISSN 1755-1307. - EISSN 1755-1315. |
| Внешние системы: | DOI: 10.1088/1755-1315/386/1/012038; РИНЦ: 43225631; SCOPUS: 2-s2.0-85077595635; |
| Реферат: | eng: Optimal assessment of geophysical fields with observational data and a mathematical model is a data assimilation problem. To solve it, a Bayesian approach is most often used. In the ensemble algorithms, ensembles of forecasts and observations are used to approximate the covariance matrices considered in the algorithm. If all probability densities are Gaussian, the problem is reduced to that of the ensemble Kalman filter. In the strongly non- Gaussian case, a particle method is used, which is based on a Bayesian approach. Ensembles are also used in this method. In the research devoted to the ensemble Kalman filter much attention is paid to deviation of ensemble elements from an average value - ensemble spread. In this paper, a comparative analysis of spread behavior over time when using different approaches to the improvement of the convergence of the algorithms is performed. The results of numerical experiments with a 1-dimensional test model are discussed. These results show that in stochastic filters the behavior over time of ensemble spread is close to the theoretical error estimate. Some of the approaches that improve convergence in the ensemble filter, such as additive inflation and multiplicative inflation, change the general formula for ensemble spread. © Published under licence by IOP Publishing Ltd. |
| Ключевые слова: | Bayesian networks; Covariance matrix; Environmental technology; Stochastic systems; Kalman filters; Probability densities; Numerical experiments; Multiplicative inflations; Ensemble Kalman Filter; Ensemble algorithms; Covariance matrices; Comparative analysis; Bayesian approaches; |
| Издано: | 2019 |
| Физ. характеристика: | 012038 |
| Конференция: | Название: Международная молодежная школа и конференция по вычислительно-информационным технологиям для наук об окружающей среде Аббревиатура: CITES '2019 Город: Москва Страна: Россия Даты проведения: 2019-05-27 - 2019-06-06 |