Lesson Plan for Week 11

Objectives

We continue to work with Xarray and will be introducing some more data processing concepts. We have already encountered aggregation-methods like groupby or rolling averages in pandas. Because environmental data has a lot of spatiotemporal variability, we frequently use methods to aggregate data in space and time to calculate climatologies, anomalies, or to smooth data.

This helps us to better understand the underlying patterns and signals in the data as well as to estimate what is “normal” for a given location and time of year.

We have also seen how to access data from AWS using pooch and we will continue to work with this to access satellite data from NOAA.

As a next step, we will start to correlate different variables and understand how different climate variables affect each other. To do so, we will calculate the El Niño Southern Oscillation (ENSO) index and correlate it with the precipitation data we have been working with.

We are now also using data primarily observed by satellites.

Specific learning goals

Technical

• Selecting and subsetting variables from an xarray- dataset.
• Plotting xarray data on maps.
• Performing aggregation options like .rolling() or .groupby() to process gridded
• Use pooch to access NOAA Climate Data Products on Amazon Web Services.
• Calculate the correlation between two variables in an xarray dataset.
• Visualize the correlation between two variables

Weather and Climate System

• Use data to calculate the El Niño Southern Oscillation (ENSO) index, which is a measure of the strength of the ENSO phenomenon.
• Calculate the correlation between the ENSO index and precipitation data to understand how ENSO affects regional precipitation patterns.

Class Preparation

Readings and Materials

Background

• Climate Match: We will be working through two tutorials from Climate Match, an open source bootcamp for using python for climate science.

  • Tutorial 6: Large Scale Climate Variability - ENSO
  • Tutorial 7: Impact of Climate Variability - Precipitation

    • We will be working through these two tutorials together. It is a good idea to have a look at them before class and to make notes on any questions you might have and that we can clarify together in class.
      

    • You should also watch the two introductory videos:

Data:

This week also makes use of some climate data observed by satellite data that is published by NOAA and freely accessible on Amazon AWS.

Climate Match has a good overview on datasets from different providers that provide climate and environmental data.

• Climate Match: Introduction to Remote Sensing
• Climate Match: Finding Satellite Climate Data

Planned Agenda

Monday:

• TBD

Wednesday:

• Semester Project Check-In.