Phases of Applications Progress

The Cal-Adapt: Analytics Engine team identified five key data applications for the energy sector through stakeholder engagement with sector representatives.

Developed (updates ongoing)

  • Introduction to retrieving, visualizing, and exporting climate data using Python and the Analytics Engine
  • Explore uncertainty within climate models due to internal variability in the climate system, using projected changes in extreme precipitation across different climate model simulations
  • Explore uncertainty across climate models, using projected variations in air temperature trends across different climate model simulations
  • Perform calculations and explore visualizations of threshold exceedance events using an interactive graphical user interface (GUI). An extension of the topics introduced in threshold_tools_basics
  • Explore the concept of Global Warming Levels, which can be used to compare possible climate outcomes across multiple scenarios or model simulations
  • Explore data transformation and analysis options for working with climate timeseries data using a graphical user interface (GUI)

In development

  • Introduction to extreme value analysis. Demonstrates how to compute statistical values of interest related to extreme weather events
  • Explore the concept and applications of an Typical Meteorological Year to represent the mean climatological conditions over one year of hourly data


  • A tool that will help users identify the simulation that meets their needs based on their intended application
  • Climate metrics and analytics to support long-range wildfire planning and management
  • Using climate data to examine impacts on renewable energy generation and operations

Note: The description and composition of applications might evolve or change as the project progresses.

Application Foundations

Jupyter notebooks

The Analytics Engine utilizes Jupyter notebooks to be the user-facing method to showcase the variety of applications built upon tools developed as part of the ClimakitAE Python library. Each of these notebooks can be used in multiple applications focused on, but not limited to, the energy sector in California supporting ratepayers through reliable and renewable energy management.

About the notebooks

The Jupyter notebooks linked below contain example code to support the stakeholder-identified applications. Interactive notebooks are available through the Analytics Engine Jupyter Hub. The notebooks provide step-by-step functionality to access, analyze, and plot climate data available through the Analytics Engine. The notebooks can be used as-is or serve as a starting point to adapt to a specific organization’s needs, workflows, or particular applications. Python tools and interactive panels included in the notebooks provide examples for how to work with both the historical and projection data on the platform, and demonstrate how to move from the climate variables provided through the Analytics Engine to actionable information that can inform decision-making and risk assessments.

Notebook previews

  1. Getting Started: explore and subset the data available on the Analytics Engine
  2. Exploring Uncertainty in Extreme Climate Events: explore internal variability by focusing on projected changes in extreme precipitation
  3. Explore Uncertainty in Climate Data: explore model uncertainty by focusing on temperature trends across simulations
  4. Counting threshold exceedance events: understand the duration of extreme events
  5. Exploring the regional response to a warmer world: apply a global warming levels approach to understand the regional response
  6. Timeseries Transformations: analyze climate timeseries data

Notebook intended applications

All of the Jupyter notebooks available on the Analytics Engine Jupyter Hub are designed intentionally to address a one or more stakeholder-identified application. To ensure that each notebook meets the requirements for an application, each notebook begins with a brief introduction to the functions available, as well as the notebook’s intended application. The notebook’s intended application is available in the Cal-Adapt: Analytics Engine - Notebooks GitHub within each available notebook. The intended application details the overall goal of the notebook and several key take-away pieces of information a user will be able to gather from the notebook.

Note: Application and Jupyter notebook development is currently active on the Analytics Engine. Existing notebooks may be modified and updated with improved code, science, and outcomes. New notebooks are added to the Analytics Engine frequently to meet additional applications.