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ATMOS 6500 Numerical Weather Prediction Fall Semester, 2013 Prof. Zhaoxia Pu Instructor: Dr. Zhaoxia Pu
Office: 712 WBB
Tel. : (801)-585-3864
E-mail: Zhaoxia.Pu@utah.edu
URL: http://www.inscc.utah.edu/~pu Lecture
hours: MW 11:50am-01:10pm Classroom: WBB 711 Office
hours: MW 01:10pm-01:40pm Course
description: Solid foundation in
atmospheric modeling and numerical weather prediction: numerical methods for
partial differential equations, an introduction to physical parameterizations,
modern data assimilation, and predictability. Course
goals: This course should provide you with
a solid foundation in understanding modern numerical weather prediction,
concentrating on basic concepts of atmospheric modeling, data assimilation, and
predictability. Prerequisite: Graduate standing and ATMOS 6010 (Dynamic Meteorology) or
instructor's consent. Textbook: Eugenia Kalnay, Atmospheric
Modeling, Data Assimilation and Predictability, Cambridge University Press, 2003, 341pp. Reference:
Thomas Warner, Numerical Weather and Climate Prediction , Cambridge University Press, 2011, 548pp Computer
lab and homeworks: There will be up to 6 major homework/lab
sets. We will practice with simple models and test basic concepts with matlab. Part of the lab work
will be done during the class. A brief programming tutorial (Matlab) will be offered at beginning. Grading policy: 40% Homework Assignments
30% Mid-term presentation 20% Final project 10% Class and Lab Participation Final grades are based on the
following scale: >90 % guarantees an A or A- >80 % guarantees a B+, B, or B- >70 % guarantees a C+, C, or C- >60 % guarantees a D+, D, or D- <60% results in an E Lecture Topics 1. Introduction History of NWP NWP processes and components 2. Fundamentals of NWP models Governing equations Filtering and scaling Vertical coordinates Numerical methods to solve
PDEs Model type, resolution and
boundary conditions 3.Physical processes and parameterizations Subgrid-scale processes Overview of model parameterizations 4. Examples of regional and global
models Community models: WRF NCEP GFS 5. Data assimilation Data source and quality
control Optimal interpolation (OI) and
objective analysis Variational data assimilation Ensemble Kalman filter Dynamical and physical balance
in initial conditions Applications and future direction
6. Atmospheric predictability and
ensemble forecasting Atmospheric predictability Error growth dynamics and limit of
predictability Operational ensemble forecasting Computer Lab Topics 1.
Familiarization with Unix and Matlab 2.
Solve simple PDEs 3.
Practice numerical methods with a simple numerical model 4.
Practice data assimilation with a sample program Disabilities Act: The
University of Utah seeks to provide equal access to its programs, services and
activities for people with disabilities. If you will need accommodations in the
class, reasonable prior notice needs to be given to the Center for Disability
Services, 162 Olpin Union Building, 581-5020 (V/TDD). CDS will work with you
and the instructor to make arrangements for accommodations. All
written information in this course can be made available in alternative format
with prior notification to the Center for Disability Services. |