ATMOS 5500

Numerical Weather Prediction

Fall Semester, 2021

Prof. Zhaoxia Pu

Instructor: Dr. Zhaoxia Pu

Office: 712 WBB

Tel.  (801)-585-3864

E-mail: Zhaoxia.Pu@utah.edu

URL: http://home.chpc.utah.edu/~pu

Lecture hours: MW 09:10 am- 10:30 am (Aug. 23 -- Oct. 08, 2021)

Classroom: IVC & Hybrid (Please check the Canvas system)

Office hours: By appointment

Class web site: http://home.chpc.utah.edu/~pu/5500.htm

Course description: This course provides students with an solid introduction to modern numerical weather forecasting techniques, concentrating on model fundamentals, structures, dynamics, physical parameterization, model forecast diagnostics, and new developments in data assimilation and data science.

Prerequisite: ATMOS 5100 (Atmospheric Dynamics) or instructor's consent.

Textbook (no textbook is required; Handouts will be provided). A useful reference:

Eugenia Kalnay, Atmospheric Modeling, Data Assimilation and Predictability, Cambridge University Press, 2003, 341pp.

Computer lab and homework: There will be up to 4 major homework sets, which will help you to get familiar with NWP process and products.

Homework may include practice problem sets with sample programs written in Matlab/Python (will be provided).

Grading policy:

50% Homework assignments

15% Class and Lab Participation

35% Final Review (Open book)

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

NWP Concepts

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. Introduction to data assimilation and ensemble forecasting

5. Hands-on experience with an NWP model

6. New developments: An introduction to machine learning

Computer Lab and Homework Topics

1. Familiarization with online NWP COMET modules

2. Solve simple PDEs with sample programs

3. Practice data analysis

4. Diagnosing model outputs

5. Hands-on experience with an NWP model

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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.