Overview

Overview

Teaching: min
Exercises: min

Questions

• What will this class cover?

Objectives

• To provide an overview of what this class is about

Overview

How to process, analyze, and interpret environmental data for climate and related disciplines. Familiarizes students with software commonly used in atmospheric research and with techniques for working with large quantities of data. Examines mathematical tools for characterizing global physical data sets which vary in time and space, and applies the tools to observations and numerical model output.

At the end of this course,learners will be able to:

• Work comfortably from the Unix command line
• Read a variety of climate data formats and make maps of the data
• Handle large simulation and re-forecast datasets
• Perform basic set of statistical analyses on climate datasets, including:
  • climatologies and anomalies
  • monthly and seasonal means and variances
  • correlation and autocorrelation,
  • regressions between a climate index and global fields
  • correlation and autocorrelation,
  • regressions between a climate index and global fields
  • composites
  • climate patterns calculated via EOFs
• Calculate statistical significance (i.e. t-test, f-test) and graph maps with a mask/stippling.
• Write codes in Python and use Jupyter notebooks
• Utilize good programming practices
• Debug, problem solve, and simplify problems
• Make publication/public quality plots
• Develop and maintain their own Github repository of climate data analysis tools and codes from this course

Key Points

• Learners will complete this course with their own toolbox