The cae-notebooks repository consists of Jupyter notebooks that provide step-by-step functionality to access, analyze, and visualize climate data availale on the Cal-Adapt: Analytics Engine. Tools 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 evaluate risks.
Notebooks are organized by similar-themed notebooks:
- Exploratory notebooks investigate a climate data topic but have no specific tools associated with the notebooks
- Including model_uncertainty.ipynb and internal_variability.ipynb
- Including model_uncertainty.ipynb and internal_variability.ipynb
- Tool notebooks highlight a specific tool
- Including warming_levels.ipynb, timeseries_transformations.ipynb, and threshold_exceedance.ipynb
- Including warming_levels.ipynb, timeseries_transformations.ipynb, and threshold_exceedance.ipynb
- Collaborative notebooks were co-produced with stakeholders for a specific application, and may be of interest to all users
- Including localization_methodology.ipynb, annual_consumption_model.ipynb and station_hourly_profiles.ipynb (8760s)
New to the Analytics Engine? Try getting_started.ipynb for a step-through of the basic functionality.
The notebooks are designed to be used as-is or serve as a starting point to adapt to specific needs, workflows, and particular applications.
Example notebooks:
timeseries_transformations.ipynb: Demonstrates pulling up a toolkit for examining and processing time-series data. Motivated by Hourly climate use case (for future time periods), for inputs into production cost, energy load forecasting, and other models.threshold_basics.ipynb: Demonstrates some functionality for calculating extreme value statistics. Motivated by the 'threshold-based analytics' use case for asset-by-asset vulnerability assessments and updating design standards.