https://github.com/cemac/ebm
Estimate parameters of stochastic energy balance models
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Repository
Estimate parameters of stochastic energy balance models
Basic Info
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Metadata Files
README.md
EBM
This project will initially be a Python port of the R package EBM for maximum likelihood estimation of k-box stochastic energy balance models. In the future, it will serve as a base upon which to add new methodologies and features as and when they are developed.
How to cite
Cummins, D. P., Stephenson, D. B., & Stott, P. A. (2020). Optimal Estimation of Stochastic Energy Balance Model Parameters, Journal of Climate, 33(18), 7909-7926, https://doi.org/10.1175/JCLI-D-19-0589.1
Quickstart
The easiest way to try out EBM is to clone this repository and build a fresh conda environment from the YAML file.
bash
git clone git@github.com:cemac/EBM.git
cd EBM
conda env create -f EBM.yml
conda activate EBM
You can then import EBM as a Python module from within the interpreter.
python
import energy_balance_model as ebm
The file demo.py contains a script showing how to generate synthetic data from a three-box stochastic EBM and how to estimate the EBM's parameters via maximum likelihood.
Licence
EBM is licenced under the MIT license - see the LICENSE file for details.
Acknowledgements
Thanks to Chris Smith for providing ensembles of calibrated parameter values, which we use here for initialisation and (optionally) regularisation of the maximum likelihood estimation.
References
Cummins, D. P., Stephenson, D. B., & Stott, P. A. (2020). Optimal Estimation of Stochastic Energy Balance Model Parameters, Journal of Climate, 33(18), 7909-7926, https://doi.org/10.1175/JCLI-D-19-0589.1
Smith, C. (2024). FaIR calibration data (1.4.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10566646
Smith, C. (2024). FaIR calibration data (1.4.3) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.13951079
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