Woodwell Risk CDD and HDD Monitor and Forecast

This site displays an estimate of historical cooling and heating degree days (CDD and HDD, respectively) along with an experimental 6-month forecast. Note that the a 'degree days' metric is normally calculated with daily data and aggregated at the monthly or yearly level, whereas we are attempting to estimate monthly degree days from monthly temperature data.

Python environment

To recreate the conda environment we use in this repository, please run:

conda env create -f environment.yml

And to activate the environment:

conda activate shiny

Run the app locally

To start a local server and see the app, please run the following command from within the app/ directory:

shiny run --reload app.py

Data sources and processing steps

Vector data

National and state outlines were downloaded from Natural Earth. Crop masks were created using a modified version of the SPAM 2020 combined rainfed- and irrigated production data for specific crops.

Raster data

The temperature data used to create the water CDD estimates comes from ERA5 monthly averaged data and were downloaded using the Copernicus Climate Data Store (CDS) Application Program Interface (API), or CDS API.

For back-end data analysis/transformation of NetCDF and TIF files, we used Python.