Advanced topics in data assimilation:…

START:
January 17, 2017
DURATION:
TR 1:00-2:15
ID:
METR 6313

INSTRUCTORS:

Xuguang Wang
Associate Professor; Presidential Research Professor

Address

National Weather Center, Room 5720, 120 David L. Boren Blvd, Norman, OK 73072   View map

Categories

Spring 2017

Time: Tuesday and Thursday 1:00-2:15
Room: NWC 5720
Instructor: Wang

When and where:  Tuesday and Thursday 1:00-­‐2:15pm, NWC 5720

Office hours:  Tuesday 2:15-­‐3pm, NWC 5341 or upon email request

Prerequisites:    MATH 3113 (ODE), 4163 (PDE), and 3333 (linear algebra); ENGR 3723 (numerical methods) or equivalent or permission of instructor.  Programming experience is useful. Not limited to Meteorology  students.

Reference texts:

Course notes.

Selected journal articles.

  1. Kalnay, 2002 (or later edition): Atmospheric Modeling, Data Assimilation and Predictability.
  2. Lewis, S. Lakshmivarahan, and S. K. Dhall, 2006 (or later edition): Dynamic data assimilation: A least square approach.
  3. Daley, 1995 (or later edition): Atmospheric Data Analysis.

Grading policy:

In-­‐class exams (40%): Mid-­‐term exam (15%), Final exam (25%)

4-­‐5 Homework assignments (40%), due in class two weeks after assigned    In-­‐class presentation (20%)

Objectives

The course is designed to introduce students to the world of ensemble Kalman filter data assimilation technique (EnKF), an advanced data assimilation method that has become popular in Meteorology, hydrology, Ecology, etc.  The students will learn the most popular EnKF techniques through lectures and hands-­‐on project assignments, and learn the EnKF applications in different fields through in-­‐class presentations and discussions. The students will not only learn various EnKF techniques and their applications, but also develop their skills in scientific thinking and synthesis, written and oral communication throughout the course

Tentative  topics:

Basic concepts of data assimilation

Mathematical preparation: matrix algebra

Least square and Bayesian contexts

Brief review of statistical interpolation, 3DVAR and 4DVAR    Classic Kalman filter and Extended Kalman filter

Basic concepts of Ensemble Kalman filter

Ensemble Kalman filter with perturbed observations    Ensemble square root filter

(Local) Ensemble Transform Kalman filter

Common problems and treatments in ensemble Kalman filters

Hybrid ensemble-­‐variational method, ensemble smoother and other special topics    Applications of ensemble Kalman filters

Guest lectures

120 David L Boren Blvd., Suite 5900, Norman, OK 73072 (405) 325-6561
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