The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Jupyter Notebook requires Python 2.7, or Python 3.3 or greater. Jupyter strongly recommends installing Python and Jupyter using the Anaconda Distribution, which includes Python, the Jupyter Notebook, and other commonly used packages for scientific computing and data science.
The notebook in this use case opens and extracts the surface air temperature from two different reanalyses (MERRA2 and ERA-Interim in this example), calculates the global monthly mean, and plots a graph for the reanalysis period.
This notebook is referenced in:
Potter, G., L. Carriere, J. Hertz, M. Bosilovich, D. Duffy, T. Lee, and D. Williams, 2017: Enabling Reanalysis Research Using the Collaborative Reanalysis Technical Environment (CREATE). Bull. Amer. Meteor. Soc. doi:10.1175/BAMS-D-17-0174.1
This use case also requires Community Data Analysis Tools (CDAT), a front-end to a rich set of visual-data exploration and analysis capabilities well-suited for data analysis problems. CDAT can be installed easily using Anaconda.
If you already have Jupyer Notebook installed, download the Global Monthly Mean Jupyter Notebook.
Download and install the Anaconda 5.1 distribution for Windows, macOS or Linux. Python 2.7 and Python 3.6 are available. Installation instructions are linked on the download page just below the Download buttons.
Note: Two installation methods are described on the page. We've experienced minor issues using the environment files provided, you may prefer installing from the conda channels if you're not an experienced Python user. When the installation is complete continue with these steps:
jupyter-notebookA browser window showing the Jupyter home page will open. If a browser is not yet installed (which may happen in fresh distribution installations) refer to your particular Linux distribution's instructions for installing a browser.
An abbreviated set of these steps are listed in the Global Monthly Mean Jupyter Notebook. Contact email@example.com with questions or for support.
If you're new to Jupyter Notebooks and want to try out simple changes to the output plot, see the comments in the notebook to locate the latitude range, time range or the reanalyses being compared. Browse the CREATE-IP catalog for available reanalyses and the CREATE-IP Global Reanalyses page for variables and time range information.
For more resources go to all things CDAT for information on the project, tutorials and support.
The NCCS has built a server side analytics tool, EDAS, to allow researchers to leverage our compute power to analyze large datasets located at the NCCS through a web-based interface, thereby eliminating the need to download the data. The CREATE data is available through EDAS and there are multiple example Jupyter Notebooks available in the Example Code section of the page.