Getting Started

Download or generate emission inventories

Four-dimensional air traffic emission inventories are essential inputs to OpenAirClim. If this is your first time using OpenAirClim, we recommend using the example or artifically generated emission inventories. Functionality to convert existing emission inventories can be found at the OpenAirClim-addon gedai. If you have any questions or issues with these conversion tools, please use gedai’s Issues workflow on Github.

The emission inventories which were created as part of the DLR internal Development Pathways for Aviation up to 2050 (DEPA 2050) project comprise realistic emission data sets for global air traffic in 5-year steps between 2020 and 2050. These example inventories can be accessed at Zenodo and downloaded using the commmand line:

zenodo_get https://doi.org/10.5281/zenodo.11442322 -o "example/input/"

Depending on the settings chosen in the configuration file, the computational time of the configured simulations could be long. If you are more interested in testing or developing OpenAirClim, you might want to use artifically generated data instead. To do so using the build-in random generator, execute the following commands:

cd utils/
python create_artificial_inventories.py

The script create_artificial_inventories.py creates a series of emission inventories comprising random emission data.

It is also possible to create emission inventories from other sources, such as from ADS-B data or using a trajectory generator. Please check out the OpenAirClim-addon gedai if you are interested in this. However, be aware that generating OpenAirClim-compatible emission inventories in such a manner can be time-consuming and computationally expensive.

Create input data

Depending on your use case, you may need to scale or normalize your emission inventories over time. A common example would be that you have a global emission inventory for a certain year, for example 2020, and want to simulate the aviation industry’s emissions over time, say between 1940 and 2200. In this case, you can scale your emission inventory along a time-dependent scenario, starting from 0 in 1940 and, for example, increasing by x% per year. In OpenAirClim, this can be achieved using either a scaling or normalization. To understand the difference between normalization and scaling, see Time Evolution.

Example normalization and scaling files can be created using the following commands:

cd utils/
python create_time_evolution.py

The script create_time_evolution.py creates two time evoluation files that control the temporal evoluation of the emission data, one for normalization and the other for scaling. These files are added to the directory example/input. To include the files in the OpenAirClim run, the file location must be provided in the configuration .toml file as the time.file variable. The example.toml file has the following lines commented out:

[time]
# ...
# file = "time_scaling_example.nc"
# file = "time_norm_example.nc"

To use either scaling or normalization, simple uncomment one of the lines.

Create test files

If you are planning on contributing to the development of OpenAirClim, you will probably need to execute the pytest functions. These require additional test files, which you can create using:

python create_test_files.py

Usage

After installation, the package can be imported and used in Python programs:

import openairclim as oac

Refer to the example folder within the repository for a minimal example and the Demonstrations given on this website.