Citations and conditions of use

Conditions of use

CHEEREIO is completely free and provided under a highly permissive MIT license. That said, we have a few requests if you use CHEEREIO:

1. Don’t be a stranger: Send me an email about how you are using CHEEREIO (andrew.pendergrass [at] duke [dot] edu). This helps me decide future development priorities, and will help me better support you through the inevitable challenges that will emerge in data assimilation applications.

2. Cite appropriately: If you are submitting a paper for publication, see the below CHEEREIO papers and recommended citations section for a list of references that have introduced CHEEREIO features. Please cite all papers that introduce features you use (in addition to the original 2023 CHEEREIO paper) as a way of recognizing efforts from the community.

3. Ask for help, preferably through a GitHub issue: I am happy to help and I usually reply within one working day. All problems and questions, no matter how minor, should reported by opening an issue on Github, as this will allow all users to see the solution. You can also email me.

4. If you have gotten significant help using CHEEREIO, consider offering coauthorship. Co-authorship is not a condition of CHEEREIO use. We only ask that you cite papers appropriately. However, if you’ve gotten significant support on debugging CHEEREIO, interpreting output, or introducing new features, consider offering co-authorship to those who helped you.

5. Offer your contributions to the community by making a GitHub pull request. If you’ve added a new observation operator or otherwise made code adjustments, be in touch via email and make a pull request on GitHub. This will allow me to add your code to a future CHEEREIO release; I’ll also update the citation section so that your efforts are recognized by future users of your code. If you need help making a pull request, just send an email to andrew.pendergrass [at] duke [dot] edu.

CHEEREIO papers and recommended citations

All CHEEREIO users should cite the original CHEEREIO paper in any publication:

Pendergrass, D. C., Jacob, D. J., Nesser, H., Varon, D. J., Sulprizio, M., Miyazaki, K., & Bowman, K. W. (2023). CHEEREIO 1.0: A versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model. Geoscientific Model Development, 16(16), 4793–4810. https://doi.org/10.5194/gmd-16-4793-2023

All CHEEREIO users should also cite the original LETKF paper in any publication:

Hunt, B. R., Kostelich, E. J., & Szunyogh, I. (2007). Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D: Nonlinear Phenomena, 230(1), 112–126. https://doi.org/10.1016/j.physd.2006.11.008

CHEEREIO users that use TCCON or TROPOMI CO (version 1.4+) should cite the following paper:

Voshtani, S., Jones, D. B. A., Wunch, D., Pendergrass, D. C., Wennberg, P. O., Pollard, D. F., Morino, I., Ohyama, H., Deutscher, N. M., Hase, F., Sussmann, R., Weidmann, D., Kivi, R., García, O., Té, Y., Chen, J., Anderson, K., Stevens, R., Kondragunta, S., … Murata, I. (2025). Quantifying CO emissions from boreal wildfires by assimilating TROPOMI and TCCON observations. Atmospheric Chemistry and Physics, 25(21), 15527–15565. doi:10.5194/acp-25-15527-2025

CHEEREIO users that use TROPOMI CH:sub:`4`, lognormal errors, or ObsPack should cite the following paper:

Pendergrass, D. C., Jacob, D. J., Balasus, N., Estrada, L., Varon, D. J., East, J. D., He, M., Mooring, T. A., Penn, E., Nesser, H., & Worden, J. R. (2025). Trends and seasonality of 2019–2023 global methane emissions inferred from a localized ensemble transform Kalman filter (CHEEREIO v1.3.1) applied to TROPOMI satellite observations. Atmospheric Chemistry and Physics, 25(21), 14353–14369. https://doi.org/10.5194/acp-25-14353-2025

Below are a list of papers which report results from using CHEEREIO, in reverse chronological order:

1. Li, Y, D. C. Pendergrass, D. J. Jacob, Y. Tang, J. Qiu, and B. Zheng. Trends and drivers of China’s methane emissions 2019–2024 inferred from regional atmospheric inversion. Submitted to Science Advances.

2. Pendergrass, D. C., Jacob, D. J., Balasus, N., Estrada, L., Varon, D. J., East, J. D., He, M., Mooring, T. A., Penn, E., Nesser, H., & Worden, J. R. (2025). Trends and seasonality of 2019–2023 global methane emissions inferred from a localized ensemble transform Kalman filter (CHEEREIO v1.3.1) applied to TROPOMI satellite observations. Atmospheric Chemistry and Physics, 25(21), 14353–14369. https://doi.org/10.5194/acp-25-14353-2025

3. Voshtani, S., Jones, D. B. A., Wunch, D., Pendergrass, D. C., Wennberg, P. O., Pollard, D. F., Morino, I., Ohyama, H., Deutscher, N. M., Hase, F., Sussmann, R., Weidmann, D., Kivi, R., García, O., Té, Y., Chen, J., Anderson, K., Stevens, R., Kondragunta, S., … Murata, I. (2025). Quantifying CO emissions from boreal wildfires by assimilating TROPOMI and TCCON observations. Atmospheric Chemistry and Physics, 25(21), 15527–15565. doi:10.5194/acp-25-15527-2025

4. Pendergrass, D. C., Jacob, D. J., Nesser, H., Varon, D. J., Sulprizio, M., Miyazaki, K., & Bowman, K. W. (2023). CHEEREIO 1.0: A versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model. Geoscientific Model Development, 16(16), 4793–4810. https://doi.org/10.5194/gmd-16-4793-2023