EaR³T

Warning

This documentation is under active development.

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The Education and Research 3D Radiative Transfer Toolbox (EaR³T, /ɜːt/) is a Python software package developed for cutting-edge radiative transfer and remote sensing applications. It provides high-level interfaces to automate the process of performing IPA/3D radiative transfer calculations for measured or modeled cloud/aerosol fields using publicly available IPA/3D radiative transfer models including MCARaTS, libRadtran (IPA only), and SHDOM (under development).

EaR³T is publicly available at https://github.com/hong-chen/er3t and licensed under GPLv3.

Applications

  • Spaceborne and airborne remote sensing;

  • 3D radiative effects (of clouds, aerosols, and trace gases etc.);

  • Synthetic data generation (for CNN training);

  • Novel retrieval algorithm development (e.g., CNN-based).

Authors and Contributors

EaR³T was originally designed and developed by Hong Chen and Sebastian Schmidt at the University of Colorado Boulder in 2019. Hong Chen is the current maintainer.

Additionally, EaR³T has been actively contributed by the following developers:

Community Support

We believe only we together can we do better and go futher. Thus we created a Discord server with aspiration to foster an informal radiative transfer and remote sensing focused community and to facilitate discussions and collaborations. Please join us on Discord to hang out together.

How to Cite

If you find EaR³T is helpful and would like to cite it, here are the references:

  • Publication

    Chen, H., Schmidt, K. S., Massie, S. T., Nataraja, V., Norgren, M. S., Gristey, J. J., Feingold, G., Holz, R. E., and Iwabuchi, H.: The Education and Research 3D Radiative Transfer Toolbox (EaR³T) - Towards the Mitigation of 3D Bias in Airborne and Spaceborne Passive Imagery Cloud Retrievals, Atmos. Meas. Tech., 16, 1971–2000, doi:10.5194/amt-16-1971-2023, 2023.

  • Software

    Chen, H., Schmidt, K. S., Nataraja, V., Hirata, K., and Chen, Y-W: hong-chen/er3t: er3t-v0.1.1 (v0.1.1), Zenodo [code], https://doi.org/10.5281/zenodo.4093737, 2023.



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