ATMOS 5500

Numerical Weather Prediction

Fall Semester, 2021

Prof. Zhaoxia Pu



Instructor:  Dr. Zhaoxia Pu

                     Office: 712 WBB

                     Tel.  (801)-585-3864




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:


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


COVID Guideline

According to the CDC, wearing a mask remains an effective means of preventing infection for both unvaccinated and vaccinated people. Regardless of what someone chooses (mask or no mask), the university seeks to foster a sense of community and asks everyone on campus to be respectful of individual decisions on mask wearing.


Vaccination is proving highly effective in preventing severe COVID-19 symptoms, hospitalization, and death from coronavirus.  Vaccinations are available to everyone 12 years and older. Appointments are open in the U of U Health system for patients as well as additional vaccine providers throughout Utah. For up-to-date campus vaccination information go to:


Safety Act

Theversity of Utah values the safety of all campus community members. To report suspicious activity or to request a courtesy escort, call campus police at 801-585-COPS (801-585-2677). You will receive important emergency alerts and safety messages regarding campus safety via text message. For more information regarding safety and to view available training resources, including helpful videos, visit


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.