Course Description
Introduction to the concepts, theory, and computational methods of data assimilation in the atmospheric and related sciences. Topics include the history of data assimilation, the “rejection problem,” adjustment to balance, balance constraints, nonlinear normal mode initialization, variational data assimilation, Kalman filter methods and applications to different disciplines and data types.
Additional Requirements for Graduate Students:
Graduate students will be required to engage in integrative
research projects that explore the current literature and
unsolved questions related to data assimilation topics covered
in this course. These projects may include investigating and/or
simulating the impacts of data assimilation on the forecasting
of weather events. Alternatively, research projects may involve
application of data assimilation to topics and fields beyond the
scope of lecture material, e.g. ecology, hydrology, space
weather, hydrology, or oceanography. In addition, exams for
graduate students will require synthesis and critique of
material via additional questions requiring more mathematical
sophistication than is expected of undergraduate students.
Athena Title
Introduction Data Assimilation
Prerequisite
GEOG(ENGR) 4112/6112 or permission of department
Semester Course Offered
Offered spring
Grading System
A - F (Traditional)
Course Objectives
An increased understanding of the fundamental theory of data assimilation and its application to the modeling of a wide range of phenomena in the atmosphere and other complex systems. An appreciation of the physical processes relevant to modeling on a variety of scales and in a variety of locations, from global scales to the mesoscale and from the tropics to the high latitudes. An ability to comprehend the dynamic processes of the atmosphere and other complex systems, such as geostrophic adjustment, that are closely related to problems in data assimilation. An opportunity to engage in inquiry-based learning via use and analysis of computer modules that simulate data assimilation in low-order dynamical models. This course meets the following General Education Abilities, by accomplishing the specific learning objectives listed below: Communicate effectively through writing. This is met by a series of writing assignments associated with supplemental reading of and writing about the data assimilation literature. Communicate effectively through speech. This is met by oral presentations, discussion leading, and classroom participation. Computer Literacy is addressed through computer-based laboratory assignments that require programming knowledge. Critical Thinking is central to the learning objectives of this class, and is developed through homework assignments, lecture, and classroom discussion. Moral Reasoning (Ethics) is addressed as it explores the application of scientific theory to real-world problems. Moral reasoning is developed through lectures, writing assignments, and classroom discussion.
Topical Outline
Overview of atmospheric dynamics History of data assimilation: Richardson’s failed forecast The initialization problem: history, the “rejection problem,” and examples using shallow-water, quasi-geostrophic, and nonlinear balance models Nonlinear normal mode initialization Variational data assimilation: 3DVAR and 4DVAR, weak and strong constraints Dynamic initialization Kalman and ensemble Kalman filters Satellite data assimilation: a success story Applications of data assimilation to other disciplines The course laboratory section will expose the students to the computational aspects of data assimilation. Students will use and modify modules created as part of the Data Assimilation Research Testbed (DART) housed at the National Center for Atmospheric Research (NCAR) in Boulder, CO. This Matlab-based software allows the students to explore the use and modification of data assimilation methods with simple theoretical models of the atmosphere devised by chaos theorist Edward Lorenz and others.
Syllabus