taobrienlbl / clemson_teca_tutorial_2023

Files for the Clemson Climate Extremes 2023 TECA Tutorial

Toolkit for Extreme Climate Analysis (TECA) Tutorial

2023 Clemson Climate Extremes Workshop

What will I learn?

My main goal is that you come away from this tutorial inspired and enabled to use the Toolkit for Extreme Climate Analysis (TECA) in your own research. Specifically, by the end of this tutorial, you will be able to:

• apply existing TECA extremes detection tools to a climate dataset
• write a custom TECA application using Python
• describe the process involved in creating a new TECA algorithm and find where to learn more

How will I learn?

This tutorial will use a combination of short lectures interspersed with lengthy practical exercises on a real supercomputing system.

What do I need to know in advance?

This tutorial assumes that participants

• are proficient in the use of Unix-type command line systems
• have some familiarity with programming (Python experience isn’t strictly necessary; if you know R, for example, the skills should be transferable for this tutorial)
• have some experience with netCDF-based climate data

Who is leading the tutorial?

I’m Travis A. O’Brien, an Assistant Professor at Indiana University Bloomington and Visiting Faculty at Lawrence Berkeley National Lab. My research focuses on understanding the factors that control variability and trends in extreme weather. Along with Dr. Mark Risser, I co-lead the Computational and Statistical Infrastructure team within the Calibrated and Systematic Characterization, Attribution, and Detection of Extreme (CASCADE) project, which is the main project that sponsors the development of TECA. I am also one of the developers of TECA (Dr. Burlen Loring is the primary developer).

What will we do in the tutorial?

This three-hour tutorial will roughly follow this outline:

Duration	Mode of Instruction	Activity
15 min	Lecture	Overview of TECA and its three main ways of being used
30 min	Lab	Use teca_metadata_probe to get the properties of a large netCDF dataset
5 min	Lecture	A brief overview of the TECA Bayesian AR Detector, TECA BARD v1.0.1
40 min	Lab	Apply teca_bayesian_ar_detect to extract precipitation associated with atmospheric rivers (AR)
30 min	Lab	Use teca_temporal_reduce to extract seasonal maximum AR precipitation
10 min	Lecture	TECA algorithms and using Python to create new TECA applications
30 min	Lab	Build a TECA app that extracts a spatial subset of AR precip seasonal max, renames variables, and writes to a new netCDF dataset
20 min	Lecture	Overview of process for writing TECA algorithms and information about learning more