This Week| Earth Data Science - Earth Lab
In this lesson you will learn about that netcdf 4 data format which is a format, commonly used to store climate data. In later lessons you will learn how to open climate data using open source Python tools.| Earth Data Science - Earth Lab
In this lesson you will learn the basics of what CMIP5 and MACA v 2 data are and how global climate data are downscaled to higher resolutions to support regional analysis.| Earth Data Science - Earth Lab
MACA V2 climate data provides but historica and future predictions of climate variables using different models. Learn how to download netcdf 4 format programatically using open source Python and open the data with xarray.| Earth Data Science - Earth Lab
Historic and projected climate data are most often stored in netcdf 4 format. Learn how to open and process MACA version 2 climate data for the Continental United States using the open source python package, xarray.| Earth Data Science - Earth Lab
Climate datasets stored in netcdf 4 format often cover the entire globe or an entire country. Learn how to subset climate data spatially and by time slices using xarray and regionmask in open source python.| Earth Data Science - Earth Lab
Learn how to calculate seasonal summary values for MACA 2 climate data using xarray and region mask in open source Python.| Earth Data Science - Earth Lab
A list comprehensions in Python is a type of loop that is often faster than traditional loops. Learn how to create list comprehensions to automate data tasks in Python.| Earth Data Science - Earth Lab
Loops can be used to automate data tasks in Python by iteratively executing the same code on multiple data structures. Practice using loops to automate certain functionality in Python.| Earth Data Science - Earth Lab
Section Three| Earth Data Science - Earth Lab