ATMOS 6500

Numerical Weather Prediction

Fall Semester, 2022

Prof. Zhaoxia Pu

Instructor

Prof. Zhaoxia Pu

Office: 712 WBB; Tel.  (801)-585-3864

E-mail: Zhaoxia.Pu@uta.edu ; URL: https://home.chpc.utah.edu/~pu

Lecture hours: Mon & Wed 09:10 am-10:30 am

Classroom: IVC & Hybrid (WBB 711)

Office hours: By appointment

Course description

Around the world, all forecast centers use numerical weather prediction products to produce daily weather forecasting. This course provides students with a solid foundation in atmospheric modeling and numerical weather prediction, which includes numerical methods for partial differential equations, an introduction to physical parameterizations, modern data assimilation, and predictability.

Course goals

This course should help students build solid knowledge in understanding processes and methods involved in modern numerical weather prediction, concentrating on fundamental concepts of atmospheric modeling, data assimilation, predictability, forecasting verification, and developments in related data science.

Prerequisite

Undergraduate or graduate-level Atmospheric Dynamic, or instructor's consent. 

(For non-ATMOS students) Fluid Dynamics or Partial Differential Equations, or equivalent course and Instructor’s consent.

Recommended Textbook

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

Reference books

Thomas Warner, Numerical Weather and Climate Prediction, Cambridge University Press, 2011, 548pp

Computer lab and homework

There will be six major homework/lab sets. We will practice with simple models and test basic concepts with Matlab/Python. We will also practice with the Weather Research and Forecasting (WRF) regional model and the NCAR community global model (e.g., Model for Prediction Across Scales or MPAS). Part of the lab work will be done during the class. A brief programming tutorial (Matlab/Python) will be offered at the beginning.

Grading policy

40% Homework and lab assignments

30% Midterm review/presentation

30% Final project/presentation

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

      - Basic concepts of NWP

 - NWP processes and components

2. Fundamentals of NWP models

Governing equations

Filtering and scaling

Vertical coordinates

Numerical methods to solve PDEs

 NWP Model type, resolution, and numerical framework

3. Physical processes and parameterizations

 Physics and subgrid-scale processes

Overview of model parameterizations

4. Data assimilation

Data source and quality control

Optimal interpolation and objective analysis

Variational data assimilation (3DVAR/4DVAR)

Ensemble Kalman filter (EnKF)

Hybrid data assimilation methods

Dynamical and physical balance in initial conditions

Observing system development

5. Atmospheric predictability and ensemble forecasting

Atmospheric predictability

Error growth dynamics and limit of predictability

Ensemble forecasting

6.   Hands-on experience with NWP models

Hands-on experience and projects with WRF regional model

Hands-on experience with MPAS global model

7.   New developments

An Introduction to Machine Learning and its application in NWP

Future direction

Computer Lab Topics

1.   Familiarization with Unix and Matlab/Python

2.   Solve simple PDEs

3.   Practice numerical methods with a simple numerical model

4.   Practice data assimilation with a sample program

5.   Hands-on practice of the regional NWP with the WRF model

6. Hands-on practice of the global NWP with the MPAS model

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 an alternative format with prior notification to the Center for Disability Services.