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Community Multiscale Air Quality Model (CMAQ) Model - Online Version

Availability | About the Course | Meet the Trainers | Payment Info | Registration | Hotels | Prerequisites | Contact info

Availability

The table below lists the dates of the upcoming CMAQ online classes along with the enrollment status in each class. When enrollment is full, we will no longer accept applications for the class and the status column in the table will display that the class is full.

The class is subject to cancellation if there are not enough registered students. A minimum of 6 trainees must be registered to conduct a training.

About the Course

The online version of the CMAQ course uses the same materials and follows the same format as our popular classroom version of the course. The online class combines pre-recorded lectures with hands-on computer exercises to teach students how to configure and run the CMAQ Modeling System and to examine the input and output files from the software. The recorded lectures provide an overview of air quality modeling, an introduction to the CMAQ programs, and a description of the course structure. The Hands-on laboratories have each student working on a computer pre-loaded with the course training materials. The labs focus on configuring and building the CMAQ programs, examining the input and output files, and running the programs to give students practical experience using CMAQ. The advanced topics lecture provides an overview of modeling diagnostic and evaluation topics, such as sensitivity modeling and statistical performance evaluation. The class concludes with a problem solving lab that emulates a real-world modeling task. The students are presented with a set of modeling data and a brief description of the modeling task. Where the previous labs included step-by-step instructions on how to set up and run the different CMAQ programs, in the Problem Solving Case Study each student is challenged to run CMAQ with help from the lessons learned in the previous labs, the instructor, and their fellow students.

A live training facilitator and virtual office hours with experienced CMAQ modelers are available to provide the best possible experience to the students taking the class. The training facilitator is available throughout the course via an instant messaging chat room to help students work through technical questions with the training materials. Virtual office hours during each day of the course offer live chat and video conferencing with experienced CMAQ modelers. The office hours are a great opportunity for students to ask questions about the training, CMAQ, and air quality modeling.

Students have one week to complete the course and during that time are provided access to online and print versions of the CMAQ training manual, online lectures, and preconfigured virtual training computers. A virtual computer lab, available through Amazon AWS, gives each student a Linux box loaded with all of the data and software needed to complete this course.

The syllabus is shown below to highlight the range of topics covered during the course. After an introduction to the online training materials, including instructions and support for connecting to the virtual computing lab, the course starts with a lecture on the basics of CMAQ, including an overview of air quality modeling, an introduction to the CMAQ Modeling System, and a description of the course structure. The Hands-on laboratories have each student working on a computer pre-loaded with the course training materials. The labs focus on configuring and running the CMAQ programs and examining the input and output files to give students practical experience using CMAQ.

At the completion of this course, students will be able to download, configure, and run the CMAQ Modeling System. They will possess the knowledge and practical experience needed to prepare input files and run CMAQ for air quality modeling.

Pre-course Software Needs

CMAQ is a Linux command-line based system. The CMAS Center provides pre-configured Linux images through Amazon Web Services (AWS) with all of the materials needed to complete the hands-on labs for this training. Each participant will need software on their systems to remotely access their AWS images.

Windows

You need to have an X-client and SSH-client (e.g. MobaXterm) installed on your Windows machine to access remote Linux servers.

Linux/MacOS

X and SSH clients are already installed on Linux and most MacOS systems. If you don't have an X client on your Mac, download and install XQuartz.

All registered students will receive additional instructions for accessing a test AWS image and the online training materials within a week of the online course start date.

Course Schedule and Syllabus

For the online training, access to Amazon AWS images and the Sakai Course Content Manager will be available during the week of the course. One week prior to the course the students will have the opportunity to login to the Sakai Course Manager and to a test out the Amazon AWS image and test their connection to the Elluminate Live Webinar.

There are three virtual meetings when the instructor will provide live instructions to all online participants. During the first 30 minutes the instructor will give a slide presentation on the logistics and course material. The remaining time is for questions, answers and troubleshooting.

The following is a general schedule of the CMAQ Online Training interactive resources:

• Day 1 - AM Live Introduction Webinar, 10:30-11:30 am ET, Introduction to CMAQ Training Online
• Day 2 - Live Help Webinar , 1-2 pm ET
• Day 3 - Live Help Webinar, 1-2 pm ET
• Days 1-4 - Chat Room, 9 am - 5 pm, ET; CMAS Trainers will be available to moderate the course interactive discussions and answer questions

All lecture slides used during the interactive discussions will archived and available for download to the course participants.

Meet the Trainers

Sarav Arunachalam, PhD Research Professor and Deputy Director, Institute for the Environment

Dr. Arunachalam, Research Professor and Deputy Director of the Institute for the Environment at the University of North Carolina at Chapel Hill (UNC-CH) is acting Director of the Community Modeling and Analysis System (CMAS) Center, an EPA-funded center hosted at UNC-CH since 2003. Dr. Arunachalam has over 25 years of experience with multiple generations of local and regional-scale air quality models, with focus on developing and applying them for understanding atmospheric chemistry and source attribution. His recent research interests are at the intersection of emissions, air quality and public health with a strong emphasis on providing the scientific basis for air quality management, through developing both reduced-form and comprehensive modeling systems. Dr. Arunachalam leads the air quality modeling activities for the CMAS Center since 2003, and has developed and taught the CMAQ training class for a global audience. Dr. Arunachalam is also an Adjunct Professor at the UNC's Gillings School of Global Public Health's Department of Environmental Sciences and Engineering.

Liz Adams, MS Research Associate, Center for Environmental Modeling for Policy Development

Ms. Adams has over 10 years of experience in model applications, model evaluation, and debugging support to developers of air quality modeling systems. Ms. Adams prepares model documentation, and performs web-based software management, software installation and testing across multiple platforms (Linux, Mac OS X, and Windows) to support public release of the Visualization Environment for Rich Data Interpretation (VERDI) tool. Ms. Adams has installed the Sparse Matrix Operator Kernel Emissions (SMOKE) training software, data and ancillary software tools to a compute server on the Amazon Web Services (AWS) Elastic Compute Cloud (EC2) for an online SMOKE training course. Ms. Adams is experienced with using Python, QGIS, Panoply, IDV, VERDI, the Atmospheric Model Evaluation Tool (AMET), and Ncview to visualize and analyze NetCDF data from the CMAQ modeling system and comparing the results to data from the observational networks. She supports the CMAS Statistical and Graphical Analysis Tools training course by preparing, testing and porting input data and scripts for VERDI and AMET to the Virtual Computing Laboratory environment, preparing and delivering classroom lectures, and providing hands-on training support to students.

Payment Info

The course includes all training materials, preconfigured training machines, live techincal support, the current CMAQ Training Manual, and access to experienced CMAQ modelers. Payment is accepted with credit card or purchase order. Note that you are asked to make the payment or initiate the payment process (e.g. by providing the purchase order number) at the time of registration. We will send you a receipt by email to confirm the receipt of the registration and payment. If you find later that you are unable to attend to the class after registration, notify the CMAS Center as soon as possible. Please see our Payment Info page for our refund policy.

Registration

Register online to sign up for a CMAS training classes.

Prerequisites

A basic knowledge of air quality modeling concepts will help students to get the most out of this course. The course covers air quality modeling terminology and basic general concepts about air quality data, but the focus is mostly on the operational details of CMAQ. A background in air quality will provide context for the need for using CMAQ.

Knowledge of the Linux command line language and the C-shell are also prerequisites for this course. Students with a working understanding of navigating Linux directory structures, viewing/editing text files, and executing commands from a Linux command prompt will benefit more from this class than students with no Linux experience.

Students must have a computer connected to the Internet that includes a Secure Shell client and an X-Window client. Instructions for downloading and installing these clients for different operating systems will be provided before the class begins.

Contact Information

For more information on the CMAQ training, please contact Brian Naess at 919-966-9925 or email cmas@unc.edu.