PyGMET is the Python version of the Gridded Meteorological Ensemble Tool (GMET), a tool for generating gridded estimates for any meteorological variable using diverse methods. PyGMET comes with many methodological and technical differences and improvements compared to GMET.

Note: PyGMET is under active development. A formal release is expected to happen in late June. Please contact Guoqiang Tang ([email protected]) if you have any problem/question with PyGMET.

PyGMET can perform the following tasks and output all results as netcdf4 files:

• Generate deterministic estimates of any input variable defined in the configuration file using various regression methods, including machine learning methods supported by scikit-learn.
• Generate cross-validation outputs with evaluation results at station points.
• Generate probabilistic estimates of any number of ensemble members.
• Provide intermediate outputs, such as spatiotemporally correlated random fields, spatial correlation, temporal autocorrelation (lag 1), nearby station index/weights, and more.

PyGMET is built on common Python packages such as SciPy, scikit-learn, and xarray. It can be run as long as the packages listed in environment.yml or requirements.txt are installed. You can create virtual environments using the following instructions:

• Pip

cd /your/path/of/PyGMET  
virtualenv PyGMET-env  
source PyGMET-env/bin/activate  
pip install -r requirements.txt  

• Conda

conda env create -f environment.yml  
conda activate PyGMET-env  

To run PyGMET, follow these steps:

1. Prepare the configuration file.
   Use configuration files in the ./test_cases folder as templates. Refer to How_to_create_config_files.md for more details.
2. Run PyGMET
   python main.py /your/path/config_filename.toml.
3. Batch run / operational run
   When producing a dataset in a target domain or testing different method choices, PyGMET can be run in many batches (e.g., month by month). We recommend users run PyGMET in two steps to improve efficiency:
   • Test run: Run PyGMET on a test period or the first batch. Basic outputs (e.g., nearby station information, weights, and spatial correlation structures) will be generated and saved, which can be used by following batch runs.
   • Batch run: Run PyGMET without changing outpath_parent so that PyGMET can find the outputs generated in the test run.

The test case for raw GMET is directly used here. To get the test case and run it, use the following commands:

cd ../src  
python main.py ../test_cases/testcase.config.static.toml  

Jupyter Notebooks in the ./docs folder can be used to visualize PyGMET test case outputs.

PyGMET has produced two publicly available datasets:

• EMDNA: Ensemble Meteorological Dataset for North America, https://doi.org/10.20383/101.0275
• EM-Earth: The Ensemble Meteorological Dataset for Planet Earth, https://doi.org/10.20383/102.0547

• PyGMET
  Tang, G., Clark, M. P., & Papalexiou, S. M. (2022). EM-Earth: The ensemble meteorological dataset for planet Earth. Bulletin of the American Meteorological Society, 103(4), E996-E1018.
  Tang, G., Clark, M. P., Papalexiou, S. M., Newman, A. J., Wood, A. W., Brunet, D., & Whitfield, P. H. (2021). EMDNA: An ensemble meteorological dataset for North America. Earth System Science Data, 13(7), 3337-3362.

• Original GMET
  Newman, A. J., Clark, M. P., Wood, A. W., & Arnold, J. R. (2020). Probabilistic spatial meteorological estimates for Alaska and the Yukon. Journal of Geophysical Research: Atmospheres, 125(22), e2020JD032696.
  Newman, A. J., Clark, M. P., Longman, R. J., Gilleland, E., Giambelluca, T. W., & Arnold, J. R. (2019). Use of daily station observations to produce high-resolution gridded probabilistic precipitation and temperature time series for the Hawaiian Islands. Journal of Hydrometeorology, 20(3), 509-529.
  Newman, A. J., Clark, M. P., Craig, J., Nijssen, B., Wood, A., Gutmann, E., ... & Arnold, J. R. (2015). Gridded ensemble precipitation and temperature estimates for the contiguous United States. Journal of Hydrometeorology, 16(6), 2481-2500.
  Clark, M. P., & Slater, A. G. (2006). Probabilistic quantitative precipitation estimation in complex terrain. Journal of Hydrometeorology, 7(1), 3-22.