This repository contains scripts to reproduce the results of the following paper:
Enrico G. A. Antonini, Edgar Virgüez, Sara Ashfaq, Lei Duan, Tyler H. Ruggles, Ken Caldeira, "Identification of reliable locations for wind power generation through a global analysis of wind droughts", Communications Earth & Environment, 2024, https://doi.org/10.1038/s43247-024-01260-7.
If you need a copy of the paper or have any questions about the scripts, please email me at enrico.antonini@cmcc.it.
[Note: Already postprocessed data including wind power densities and energy deficits are available in the following Zenodo repository.]
To run the scripts in this repository, you need to have:
- Conda, which is an open source package management system and environment management system that comes with Anaconda or Miniconda.
- CDS API key, which is needed to download the climate data from the Copernicus' Climate Data Store.
- 6 TB of free disk space. 3 TB are needed to download the climate data and 3 TB are needed to process the climate data and calculate the wind power densities and energy deficits.
- Sufficient RAM memory to process the climate data. Downloaded climate files each containing a single variable for a single year are about 17 GB. Other derived files have dimensions up to 68 GB. I would recommend at least 128 GB of RAM memory. Some steps were intentionally made supre basic to minimize the RAM memory usage.
[Note: If you download the already postprocessed data, you only need to have Conda. The scripts for analyzing them can be run on a regular laptop.]
The environments folder contains yaml files to create conda environments for
- downloading climate data from the Copernicus' Climate Data Store,
- processing the climate data and calculate the wind power densities and energy deficits,
- analyzing the wind power densities and energy deficits and generating the figures of the paper.
A conda environment can be created by running the following command:
conda env create --file selected_environment.yml
where you need to specify the actual yaml file name.
[Note: If you download the already postprocessed data, you only need to create the environment for analyzing the final data.]
[Required conda environemt: download_climate_data]
To download the climate data from the Copernicus' Climate Data Store, you need to run the following command from within the script_and_data folder:
python step0_download_data.py
The download will take several hours, depending on your internet connection, and will require about 3 TB of disk space.
[Note: If you download the already postprocessed data, the climate data download is not necessary.]
[Required conda environemt: wind_droughts_processing]
To process the climate data and calculate the wind power densities and energy deficits, you need to run the following commands from within the script_and_data folder.
[Note: If you download the already postprocessed data, the climate data processing is not necessary.]
Get the wind speed time series in a given year and its annual mean:
python step_1_get_wind_speed_and_annual_mean_in_year.py year
where you need to specify the actual year.
Get the wind power density time series in a given year and its annual mean:
python step_2_get_wind_power_density_and_annual_mean_in_year.py year
where you need to specify the actual year.
Get the mean wind speed and mean wind power density across all years:
python step_3_get_mean_wind_speed_and_power_density.py
Initialize the climatological wind power density time series file for subsequent steps:
python step_4_create_climatological_wind_power_density_array.py
Add the wind power density time series in a given year to the climatological wind power density time series:
python step_5_add_wind_power_density_in_year.py year
where you need to specify the actual year.
Divide the climatological wind power density time series by the number of years:
python step_6_divide_climatological_wind_power_density.py
Get the energy deficit for the seasonal variability:
python step_7_get_energy_deficit_for_seasonal_variability.py
Get the energy deficit for the weather variability in a given year:
python step_8_get_energy_deficit_for_weather_variability_in_year.py year
where you need to specify the actual year.
Get the energy deficit for wind droughts:
python step_9_get_energy_deficit_for_wind_droughts_in_year.py year
where you need to specify the actual year.
Note that step 8 and 9 generate energy deficit for a specific year and save it to a single file. All the generated files should then be combined into a single file.
[Required conda environemt: wind_droughts_analysis]
To analyze the wind power densities and energy deficits and generate the figures of the paper, you need to run the following command from within the script_and_data folder:
python step_10_analyze_results.py