**Author details**

Marwa Chaabane1,2, Imen Baklouti2,4, Majdi Mansouri1\*, Nouha Jaoua2 , Hazem Nounou1 , Mohamed Nounou3 , Ahmed Ben Hamida2 and Marie‐France Destain4

\*Address all correspondence to: majdi.mansouri@qatar.tamu.edu

1 Electrical and Computer Engineering Program, Texas A&M University at Qatar, Doha, Qatar

2 Advanced Technologies for Medicine and Signals, National Engineering School of Sfax, Tunisia

3 Chemical Engineering Program, Texas A&M University at Qatar, Doha, Qatar

4 Biosystems Engineering Department, GxABT, University of Liege, Gembloux, Belgium

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148 Nonlinear Systems - Design, Analysis, Estimation and Control

Mohamed Nounou3

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Marwa Chaabane1,2, Imen Baklouti2,4, Majdi Mansouri1\*, Nouha Jaoua2

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1 Electrical and Computer Engineering Program, Texas A&M University at Qatar, Doha, Qatar

2 Advanced Technologies for Medicine and Signals, National Engineering School of Sfax,

4 Biosystems Engineering Department, GxABT, University of Liege, Gembloux, Belgium

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## **An Introduction to Ensemble-Based Data Assimilation Method in the Earth Sciences An Introduction to Ensemble-Based Data Assimilation Method in the Earth Sciences**

Youmin Tang, Zheqi Shen and Yanqiu Gao Youmin Tang, Zheqi Shen and Yanqiu Gao

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/64718

### **Abstract**

In this chapter, the ensemble-based data assimilation methods are introduced, including their developments, applications and existing concerns. These methods include both traditional methods such as Kalman filter and its derivatives and some advanced algorithms such as sigma-point Kalman filters and particle filters. Emphasis is placed on the challenges of applying these methods onto high-dimensional systems in the earth sciences.

**Keywords:** data assimilation, Kalman filter, EnOI, EnKF, particle filter
