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.
- Sources of Climate Data
- Introduction to NASA Earthdata Search and Re-Analysis Data
- Reading MERRA-2 Gridded Climate Data
- Accessing MERRA-2 Data in the Cloud
- Introduction to Earth Observation Data
- Introduction to Climate Models
- Using Re-Analysis Data to Study Drought
- Using NASA Earth Observations
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 notebookThis 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)
- 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
This curriculum was enabled by a grant from NASA's Transition to Open Science (TOPS) Training program (80NSSC23K0864), part of NASA's TOPS Program