ATMOS 5500/6500

Numerical Weather Prediction (NWP)

Fall Semester, 2023

Prof. Zhaoxia Pu

Instructor

Prof. Zhaoxia Pu

Zhaoxia.Pu@utah.edu; https://home.chpc.utah.edu/~pu

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

Classroom: WBB 711

Office hours: Right after the class or by appointment

Course description

 Around the world, all forecast centers use numerical weather prediction (NWP) products to generate daily weather forecasts. This course offers students a strong foundation in atmospheric modeling and numerical weather prediction, encompassing NWP processes and components, numerical modeling with partial differential equations and physical parameterizations, modern data assimilation, ensemble forecasting, and data science applications in NWP.

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, ensemble forecasting, forecasting verification, and developments in related data science.

Prerequisite

Undergraduate or graduate standing; (For ATMOS students) Atmospheric Dynamic, or instructor's consent. 

(For non-ATMOS students) Fluid Dynamics or Caculus III (or equivalent); Alternatively, Instructor’s consent.

Recommended Textbook

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

Reference book

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 or Python. We will also practice with the Weather Research and Forecasting (WRF) regional model. Part of the lab work will be done during the class. A brief programming tutorial (Matlab/Python) will be offered at the beginning of the semester.

Grading policy

40% Homework and lab assignments

25% Midterm review (ATMOS 5500/6500) and presentation (ATMOS 6500 only)

30% Final report (ATMOS 5500) or Project report/presentation (ATMOS 6500)

5% Attendance

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

o Fundamentals of weather forecasting (new!)

o       Basic concepts of NWP

o       NWP processes and components

2. Fundamentals of NWP models

o Governing equations

o Filtering and scaling

o Vertical coordinates

o Numerical methods to solve PDEs

o  NWP Model type, resolution, and numerical framework

3. Physical processes and parameterizations

o  Physics and subgrid-scale processes

o Overview of model parameterizations

4. Data assimilation

o Data source and quality control

o Optimal interpolation and objective analysis

o Variational data assimilation (3DVAR/4DVAR)

o Ensemble Kalman filter (EnKF)

o Hybrid data assimilation methods (new!)

o Dynamical and physical balance in initial conditions

o Observing system development (new!)

5. Atmospheric predictability and ensemble forecasting

o Atmospheric predictability

o Error growth dynamics and limit of predictability

o Ensemble forecasting (new!)

7.   Big data and data science in NWP (new!)

o Big data in NWP

o Applications of data science in NWP

8.   Hands-on experience with NWP models

o Hands-on experience (ATMOS 5500/6500) and projects (ATMOS 6500 only) with WRF regional model (new!)

Computer Lab Topics

1.   Familiarization with Unix/Linux 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 (new!)

6. Hands-on practice with sample machine-learning algorithms (new!)

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.