Description：

Data assimilation is a powerful framework for combining observational data with mathematical models to improve predictions and understanding of complex systems. Widely used in geosciences and many areas of applied science, it provides essential tools for weather forecasting, sea ice, ocean, and climate modeling, as well as a growing range of industrial applications. This course offers an introduction to the fundamental ideas and practical techniques of data assimilation. We will cover key concepts such as state estimation and filtering techniques (Kalman filters, EAKF, ETKF, etc), and explain how these methods integrate theory, computation, and data. We will explore case studies drawn from atmospheric and oceanic sciences. If time permits, we will also discuss advanced topics, such as the Lagrangian–Eulerian Multiscale Data Assimilation (LEMDA) method and nonlinear filtering strategies such as the Yau–Yau filter.

Prerequisite：

Basic knowledge of calculus, linear algebra, and statistics is recommended. Motivated first-year students are welcome after a brief discussion with the instructor.

Reference：

1. Majda, Andrew J., and John Harlim. Filtering complex turbulent systems. Cambridge University Press, 2012.

2. Law, Kody, Andrew Stuart, and Kostas Zygalakis. "Data assimilation." Cham, Switzerland: Springer 214 (2015): 52.

3. Asch, Mark, Marc Bocquet, and Maëlle Nodet. Data assimilation: methods, algorithms, and applications. Society for Industrial and Applied Mathematics, 2016.

Target Audience: Undergraduate students, Graduate students

Teaching Language: English

Registration: https://www.wjx.top/vm/Ohi8xZh.aspx#