sadamov / realtime_pollen_calibration

Realtime Pollen Calibration

This repository calculates the pollen input fields needed for the real-time pollen calibration in ICON-ART. It is very similar to the FORTRAN implementation used in COSMO-ART. More information about the Pollen module currently in the weather model COSMO can be found here: https://service.meteoswiss.ch/confluence/x/dYQYBQ And in this paper: (currently under review :)

This specific project also has a confluence page here: https://service.meteoswiss.ch/confluence/x/M_ahBw

Data Import

In the /data folder are case studies for four Species:

• Alder (ALNU)
• Birch (BETU)
• Hazel (CORY)
• Grasses (POAC)

The identify_cases folder contain text files that were used to identify timesteps with large changes in the tthrs, tthre, tune or saisl fields. Based on these files, two subsequent hourly fields were selected to form case studies. For the first hour of the pair, additional atab files are provided. Based on these atab files and the input GRIB-fields the resulting fields one hour later can be calculated.

The atabs folder contains modelled and measured hourly averages of the past 120h (5 days). Their use can be deducted from their names. The date at the end of the name corresponds to the first hour of the case study pair. Example name: alnu_pollen_measured_values_2022020805

The grib2_files_cosmo1e folder contains the GRIB2 files for each case study pair. For each species there are 3 pairs

• one for the season start tthrs/tthre
• one for the tuning tune
• for the season end tthre/saisl

Plotting

In the /notebook folder is a simple script that allows for plotting 2D-maps using xarray and iconarray.

Start developing

Once you created or cloned this repository, make sure the installation is running properly. Install the package dependencies with the provided script setup_env.sh. Check available options with

tools/setup_env.sh -h

We distinguish development installations which are editable and have additional dependencies on formatters and linters from productive installations which are non-editable and have no additional dependencies. Moreover we distinguish pinned installations based on exported (reproducible) environments and free installations where the installation is based on top-level dependencies listed in requirements/requirements.yml. If you start developing, you might want to do an unpinned installation and export the environment:

tools/setup_env.sh -u -e -n <package_env_name>

Hint: If you are the package administrator, it is a good idea to understand what this script does, you can do everything manually with conda instructions.

Hint: Use the flag -m to speed up the installation using mamba. Of course you will have to install mamba first (we recommend to install mamba into your base environment conda install -c conda-forge mamba. If you install mamba in another (maybe dedicated) environment, environments installed with mamba will be located in <miniconda_root_dir>/envs/mamba/envs, which is not very practical.

The package itself is installed with pip:

conda activate <package_env_name>
pip install --editable .

Warning: Make sure you use the right pip, i.e. the one from the installed conda environment (which pip should point to something like path/to/miniconda/envs/<package_env_name>/bin/pip).

Once your package is installed, run the tests by typing:

conda activate <package_env_name>
pytest

If the tests pass, you are good to go. If not, contact the package administrator Simon Adamov. Make sure to update your requirement files and export your environments after installation every time you add new imports while developing. Check the next section to find some guidance on the development process if you are new to Python and/or APN.

Roadmap to your first contribution

Generally, the source code of your library is located in src/<library_name>. The blueprint will generate some example code in mutable_number.py, utils.py and cli.py. cli.py thereby serves as an entry point for functionalities you want to execute from the command line, it is based on the Click library. If you do not need interactions with the command line, you should remove cli.py. Moreover, of course there exist other options for command line interfaces, a good overview may be found here (https://realpython.com/comparing-python-command-line-parsing-libraries-argparse-docopt-click/), we recommend however to use click. The provided example code should provide some guidance on how the individual source code files interact within the library. In addition to the example code in src/<library_name>, there are examples for unit tests in tests/<library_name>/, which can be triggered with pytest from the command line. Once you implemented a feature (and of course you also implemented a meaningful test ;-)), you are likely willing to commit it. First, go to the root directory of your package and run pytest.

conda activate <package_env_name>
cd <package-root-dir>
pytest

Note that neither pytest, nor pre-commit, nor any of the linters invoked by the pre-commit hooks will be available in the production environment, so make sure you have a development environment installed and activated. If you use the blueprint as is, pre-commit will not be triggered locally but only if you push to the main branch (or push to a PR to the main branch). If you consider it useful, you can set up pre-commit to run locally before every commit by initializing it once. In the root directory of your package, type:

pre-commit install

If you run pre-commit without installing it before (line above), it will fail and the only way to recover it, is to do a forced reinstallation (conda install --force-reinstall pre-commit). You can also just run pre-commit selectively, whenever you want by typing (pre-commit run --all-files). Note that mypy and pylint take a bit of time, so it is really up to you, if you want to use pre-commit locally or not. In any case, after running pytest, you can commit and the linters will run at the latest on the GitHub actions server, when you push your changes to the main branch. Note that pytest is currently not invoked by pre-commit, so it will not run automatically. Automated testing should be implemented in a Jenkins pipeline (template for a plan available in jenkins/. See the next section for more details.

Development tools

As this package was created with the APN Python blueprint, it comes with a stack of development tools, which are described in more detail on (https://meteoswiss-apn.github.io/mch-python-blueprint/). Here, we give a brief overview on what is implemented.

Testing and coding standards

Testing your code and compliance with the most important Python standards is a requirement for Python software written in APN. To make the life of package administrators easier, the most important checks are run automatically on GitHub actions. If your code goes into production, it must additionally be tested on CSCS machines, which is only possible with a Jenkins pipeline (GitHub actions is running on a GitHub server).

Pre-commit on GitHub actions

.github/workflows/pre-commit.yml contains a hook that will trigger the creation of your environment (unpinned) on the GitHub actions server and then run pytest as well as various formatters and linters through pre-commit. This hook is only triggered upon pushes to the main branch (in general: don't do that) and in pull requests to the main branch.

Jenkins

Two jenkins plans are available in the jenkins/ folder. On the one hand jenkins/Jenkinsfile controls the nightly (weekly, monthly, ...) builds, on the other hand jenkins/JenkinsJobPR controls the pipeline invoked with the command launch jenkins in pull requests on GitHub. Your jenkins pipeline will not be set up automatically. If you need to run your tests on CSCS machines, contact DevOps to help you with the setup of the pipelines. Otherwise, you can ignore the jenkinsfiles and exclusively run your tests and checks on GitHub actions.

Features

The project itself consists of two scripts: one for updating the season phenology for different pollen species and the other to update the strength of the pollen season based on observed and modelled concentrations.

• update_phenology_realtime: Takes as input an ATAB file which contains the measured pollen concentrations, a GRIB file containing the following fields: T_2M, tthrs, tthre (for POAC, saisl instead), saisn and ctsum for a pollen species, and the name of the desired output file. The output file is in grib format, advanced by 1 hour and contains the fields tthrs and tthre (for POAC, saisl instead).
• update_strength_realtime: Takes as input two ATAB file which contain the measured and modeled pollen concentrations, a GRIB file containing the following fields: tune and saisn for a pollen species, and the name of the desired output file. The output file is in grib format, advanced by 1 hour and contains the field tune.
• TODO: Explain the expected ATAB format ?

Credits

This package was created with copier and the MeteoSwiss-APN/mch-python-blueprint project template.