M1: Open Climate Data

How are NASA satellites, field data, and models used to diagnose and predict Earth’s climate system? How is climate variability measured and modeled?

The first module of our open climate-science curriculum focuses on familiarizing learners with NASA Earthdata Search and with the variety of climate datasets NASA offers. At the end of this module, you should be able to:

• Understand how climate data from reanalysis datasets, General Circulation Models, and Earth System Models are generated and how these models differ.
• Know where different climate variables (e.g., precipitation, temperature) can be obtained at the appropriate spatial and temporal scales.
• Demonstrate the use of multiple climate variables from different climate datasets.

Contents

1. Sources of Climate Data
2. Introduction to NASA Earthdata Search and Re-Analysis Data
3. Reading MERRA-2 Gridded Climate Data
4. Accessing MERRA-2 Data in the Cloud
5. Introduction to Earth Observation Data
6. Introduction to Climate Models
7. Using Re-Analysis Data to Study Drought
8. Using NASA Earth Observations

Getting Started

See our installation guide here.

You can run the notebooks in this repository using Github Codespaces or as a VSCode Dev Container. Once your container is running, launch Jupyter Notebook by:

# Create your own password when prompted
jupyter server password

# Then, launch Jupyter Notebook; enter your password when prompted
jupyter notebook

Learning Outcomes

This course covers the following Core Competencies in Computational Data Science:

• Raw data are unmodified and kept separate from any processed derivatives or analysis results. (CC1.1)
• A project's files are organized hierarchically and semantically. Raw data, processed data, code, and outputs are stored in separate folders. (CC1.2)
• Creates appropriate metadata for all datasets, including, but not limited to: the creation date, primary data sources, fill values or valid ranges, and units. (CC1.9)
• Understands multidimensional arrays and their use for representing datasets structured by space, time, and multiple variables. (CC2.3)
• Familiar with the different types of structured datasets used in scientific applications, including spatial datasets (raster and vector) and hierarchical datasets (e.g., HDF5, netCDF4); how to read them; and how to create self-documenting data files. (CC2.8)
• Chooses color scales that are perceptually linear and colorblind-friendly. Understands how visual scales relate to different types of quantitative and qualitative data. (CC3.10)
• Computational workflows are documented with both in-line comments and external documentation (a README or API documentation). (CC4.3)

Climate Datasets Used

• Daily air temperatures from the NASA Global Modeling and Assimilation Office's MERRA-2 re-analysis dataset
• Daily precipitation totals from NASA IMERG-Final
• Evapotranspiration, radiation, and soil moisture data from NASA's North American Land Data Assimilation System (NLDAS) re-analysis dataset
• Air temperature, pressure, and humidity, from NASA's NLDAS forcing data
• Soil moisture from the NASA Soil Moisture Active Passive mission

Acknowledgements

This curriculum was enabled by a grant from NASA's Transition to Open Science (TOPS) Training program (80NSSC23K0864), part of NASA's TOPS Program