Welcome to EDS 220, Fall 2021! This is the master repository for all example course materials, which will be added to throughout the course of the quarter. As we progress, parts of this repo will be cloned into smaller repo's for course assignments via Github Classrooms, but this should serve as a useful reference for us all.
You are welcome to copy these materials, make your own changes, and even fork this repository if you like! If you find that the changes you made could be useful to others, please also feel free to submit pull requests so they can be incorporated into the course for future students.
Make sure to check out these instructions for downloading/installing Anaconda, Jupyter, and the Google Earth Engine environment (and activating the GEE environment). https://docs.google.com/document/d/1P1c6Ef7D1OoGaTaOrPDO0m5LBN1okpGlU0OXKKzsoi0/edit
These notebooks provide an introduction to various environmental data sets, which should give you a good sense of the range of tools out there for manipulating and processing environmental data as well as the types of data sets that exist.
This notebook just contains some basics on setting up Jupyter on your local machine, creating a custom environment for Google Earth Engine (GEE), and making a blank map. There is substantial overlap with the next notebook, this is just here to provide some extra assistance with the initial setup.
The first exercise (Week1_ERA5Maps_GEE) provides an overview of how to install the Google Earth Engine Python API and use simple commands to visualize global precipitation data. This will allow you to map information from a global "reanalysis" (= estimate of precipitation everywhere on Earth, derived by using a numerical model that incorporates all available observational data) and display any region you think is interesting!
We continue working in the GEE environment for a more complex example: loading in "multispectral" data (radiation sensed at multiple wavelengths) from the Landsat 8 mission. This example will show you where to find and how to load Landsat data, and use it to calculate a commonly used index for vegetation growth: the Normalized Difference Vegetation Index, or NDVI.
OISST_ENSO_MHWs: Using global gridded SST products to visualize El Nino/Southern Oscillation and marine heat wave behavior
We switch to the ocean for an intensive lesson in evaluating metrics for ENSO and marine heat waves. This uses monthly data from NOAA's Extended Reconstructed SST version 5 (ERSSTv5) and daily data from the NOAA 1/4° Daily Optimum Interpolation Sea Surface Temperature version 2 (OISSTv2) datasets to calculate various things and plot them in time series and map format. This code can be run using the Binder link below!
Week8_EDS220_MultiDataset: Continuing with gridded SST products, combining with surface current information
This notebook continues the MHW analysis in OISST_ENSO_MHWs, and combines SST information with additional data on surface currents from the NCEP Global Ocean Data Assimilation System (GODAS). The aim is to identify the most persistent MHW over the past 10 years, select SST and current data within that event, and generate a combined SST/current map using data interpolated to a common grid.
