Efficient Formulation and Implementation of Data Assimilation Methods

被引：0

作者：

Nino-Ruiz, Elias D. ^{[1
]}

Sandu, Adrian ^{[2
]}

Cheng, Haiyan ^{[3
]}

机构：

[1] Univ Norte, Dept Comp Sci, Appl Math & Computat Sci Lab, Barranquilla 080001, Colombia

[2] Virginia Polytech Inst & State Univ, Dept Comp Sci, Blacksburg, VA 24060 USA

[3] Willamette Univ, Dept Comp Sci, 900 State St, Salem, OR 97301 USA

来源：

ATMOSPHERE | 2018年 / 9卷 / 07期

关键词：

ensemble Kalman filter; posterior ensemble; modified Cholesky decomposition; sampling methods; empirical orthogonal functions; Gaussian mixture models; ENSEMBLE KALMAN FILTER; VARIATIONAL DATA ASSIMILATION; NUMERICAL WEATHER PREDICTION;

D O I：

10.3390/atmos9070254

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

This Special Issue presents efficient formulations and implementations of sequential and variational data assimilation methods. The methods address three important issues in the context of operational data assimilation: efficient implementation of localization methods, sampling methods for approaching posterior ensembles under non-linear model errors, and adjoint-free formulations of four dimensional variational methods.

引用

页数：3

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