How to use climate projections data on Cal-Adapt for building web applications and visualizations

Cal-Adapt (launched 2011) provides access to the wealth of climate data produced by California’s scientific and research community through interactive climate tools, data downloads and a new public Web API. Cal-Adapt is developed by UC Berkeley's Geospatial Innovation Facility with oversight and funding from California Energy Commission. Through the public Cal-Adapt Web API, users can currently access future climate projections for the following climate variables:

  • Precipitation, maximum and minimum temperature - LOCA downscaled climate projections from Scripps Institution of Oceanography, UC San Diego
  • Snowpack (snow water equivalence) - Produced by Scripps Institution of Oceanography, UC San Diego through the application of Variable Infiltration Capacity model to LOCA data
  • Sea Level Rise - CalFloD-3D, UC Berkeley

We will use the Jupyter Notebook App and use Python commands to request data from the Cal-Adapt API, perform some simple data analysis and produce summary tables. Topics to be covered include:

  • Structuring web requests to get climate projections for a particular location or area from the Cal-Adapt Web API
  • Using the temporal aggregation and statistical summary functionalities provided by the Cal-Adapt Web API
  • Exporting data to various formats to include in reports

Link to GitHub Repo.

This workshop is open to beginners and no previous programming knowledge is required. The content will be geared towards local/state government professionals working on climate adaptation planning in California, but it is open to anyone interested in working with climate data. Participants should bring their own laptops.

You have a couple of options to get setup with Jupyter Notebook environment:

  • For new users, we highly recommend installing Anaconda. Anaconda conveniently installs Python, the Jupyter Notebook, and other commonly used packages for scientific computing and data science.
  • For experienced users, install the following Python 3 packages - jupyter, pandas, seaborn, requests
  • If you have trouble with either options, you can use the JupyterHub we plan to setup for this workshop. You only need a browser to work with our JupyterHub. Details on this will be provided during the workshop
Schedule info
Session Time Slot(s): 
Monday, May 22, 2017 - 13:00 to 15:00
Shruti Mukhtyar (Geospatial Innovation Facility, UC Berkeley)'s picture

This workshop has an additional speaker - Brian Galey ( I am unable to add him through the form.

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