| Name | SBCK JSON |
| Version |
1.3.1
JSON |
| download |
| home_page | |
| Summary | Statistical Bias Correction Kit |
| upload_time | 2023-10-04 13:59:24 |
| maintainer | |
| docs_url | None |
| author | Yoann Robin |
| requires_python | |
| license | GNU General Public License v3 |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# SBCK (Statistical Bias Correction Kit)
## Features
- python3 and R version
- c++ independent files for Sparse Histogram
- Implement classic methods of bias correction (see [8,9] for the definition of bias correction)
- Quantile Mapping [5,7,14], parametric and non parametric version
- CDFt methods [6]
- OTC and dOTC methods [9]
- R2D2 method [11]
- MBCn method [4]
- QDM method [3]
- MRec method [1]
- ECBC method [12]
- TSMBC method [15], for autocorrelations.
## How to select a bias correction method ?
This summary of ability of each method to perform a bias correction is proposed by François, (2020). Please refer to
this article for further interpretation.
| Characteristics | CDF-t | R2D2 | dOTC | MBCn | MRec |
|---------------------------------------------| :----------------: | :----------------: | :----------------: | :----------------: | :----------------: |
| Correction of univariate dist. prop. | Yes | Yes | Yes | Yes | Yes |
| Modification of correlations of the model | No | Yes | Yes | Yes | Yes |
| Capacity to correct inter-var. prop. | No | Yes | Yes | Yes | Yes |
| Capacity to correct spatial prop. | No | Yes | Yes | ~ | ~ |
| Capacity to correct temporal prop. | No | No | No | No | No |
| Preserve the rank structure of the model | Yes | ~ | ~ | ~ | ~ |
| Capacity to correct small geographical area | n.a. | Yes | Yes | Yes | Yes |
| Capacity to correct large geographical area | n.a. | ~ | ~ | ~ | No |
| Allow for change of the multi-dim. prop. | Yes | No | Yes | ~ | Yes |
## Python instruction
Requires:
- python3
- [Eigen](http://eigen.tuxfamily.org/index.php?title=Main_Page)
- numpy
- scipy
- pybind11
You can install from pypi:
```
pip3 install SBCK
```
Or from sources:
```
git clone https://github.com/yrobink/SBCK-python.git
cd SBCK
pip3 install .
```
If the Eigen library is not found, use:
```
pip3 install . eigen="path-to-eigen"
```
## Acknowledgements
Thanks to [[Trevor James Smith]](https://github.com/Zeitsperre) for his help with the publication on pypi.
## License
Copyright(c) 2021 / 2023 Yoann Robin
This file is part of SBCK.
SBCK is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
SBCK is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with SBCK. If not, see <https://www.gnu.org/licenses/>.
## References
- [[1]](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2011WR011524) Bárdossy, A. and Pegram, G.: Multiscale spatial recorrelation of RCM precipitation to produce unbiased climate change scenarios over large areas and small, Water Resources Research, 48, 9502–, https://doi.org/10.1029/2011WR011524, 2012.
- [2] Bazaraa, M. S., Jarvis, J. J., and Sherali, H. D.: Linear Programming and Network Flows, 4th edn., John Wiley & Sons, 2009.
- [[3]](https://doi.org/10.1175/JCLI-D-14-00754.1) Cannon, A. J., Sobie, S. R., and Murdock, T. Q.: Bias correction of simulated precipitation by quantile mapping: how well do methods preserve relative changes in quantiles and extremes?, J. Climate, 28, 6938–6959, https://doi.org/10.1175/JCLI-D-14-00754.1, 2015.
- [[4]](https://link.springer.com/article/10.1007/s00382-017-3580-6) Cannon, Alex J.: Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables, Climate Dynamics, nb. 1, vol. 50, p. 31-49, 10.1007/s00382-017-3580-6, 2018.
- [[5]](https://doi.org/10.1016/j.gloplacha.2006.11.030) Déqué, M.: Frequency of precipitation and temperature extremes over France in an anthropogenic scenario: Model results and statistical correction according to observed values, Global Planet. Change, 57, 16–26, https://doi.org/10.1016/j.gloplacha.2006.11.030, 2007.
- [[6]](https://doi.org/10.1029/2009GL038401) Michelangeli, P.-A., Vrac, M., and Loukos, H.: Probabilistic downscaling approaches: Application to wind cumulative distribution functions, Geophys. Res. Lett., 36, L11708, https://doi.org/10.1029/2009GL038401, 2009.
- [7] Panofsky, H. A. and Brier, G. W.: Some applications of statistics to meteorology, Mineral Industries Extension Services, College of Mineral Industries, Pennsylvania State University, 103 pp., 1958.
- [[8]](https://doi.org/10.1016/j.jhydrol.2010.10.024) Piani, C., Weedon, G., Best, M., Gomes, S., Viterbo, P., Hagemann, S., and Haerter, J.: Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models, J. Hydrol., 395, 199–215, https://doi.org/10.1016/j.jhydrol.2010.10.024, 2010.
- [[9]](https://doi.org/10.5194/hess-23-773-2019) Robin, Y., Vrac, M., Naveau, P., Yiou, P.: Multivariate stochastic bias corrections with optimal transport, Hydrol. Earth Syst. Sci., 23, 773–786, 2019, https://doi.org/10.5194/hess-23-773-2019
- [[10]](https://arxiv.org/abs/1306.0895) Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances. arXiv, https://arxiv.org/abs/1306.0895
- [[11]](https://doi.org/10.5194/hess-22-3175-2018) Vrac, M.: Multivariate bias adjustment of high-dimensional climate simulations: the Rank Resampling for Distributions and Dependences (R2 D2 ) bias correction, Hydrol. Earth Syst. Sci., 22, 3175–3196, https://doi.org/10.5194/hess-22-3175-2018, 2018.
- [[12]](https://doi.org/10.1175/JCLI-D-14-00059.1) Vrac, M. and P. Friederichs, 2015: Multivariate—Intervariable, Spatial, and Temporal—Bias Correction. J. Climate, 28, 218–237, https://doi.org/10.1175/JCLI-D-14-00059.1
- [13] Wasserstein, L. N. (1969). Markov processes over denumerable products of spaces describing large systems of automata. Problems of Information Transmission, 5(3), 47-52.
- [[14]](https://doi.org/10.1023/B:CLIM.0000013685.99609.9e) Wood, A. W., Leung, L. R., Sridhar, V., and Lettenmaier, D. P.: Hydrologic Implications of Dynamical and Statistical Approaches to Downscaling Climate Model Outputs, Clim. Change, 62, 189–216, https://doi.org/10.1023/B:CLIM.0000013685.99609.9e, 2004.
- [[15]](https://doi.org/10.5194/esd-2021-12) Robin, Y. and Vrac, M.: Is time a variable like the others in multivariate statistical downscaling and bias correction?, Earth Syst. Dynam. Discuss. [preprint], https://doi.org/10.5194/esd-2021-12, in review, 2021.
- François, B., Vrac, M., Cannon, A., Robin, Y., and Allard, D.: Multivariate bias corrections of climate simulations: Which benefits for which losses?, Earth Syst. Dyn., 11, 537–562, https://doi.org/10.5194/esd-11-537-2020, https://esd.copernicus.org/articles/11/537/2020/, 2020.
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"description": "# SBCK (Statistical Bias Correction Kit)\n\n## Features\n- python3 and R version\n- c++ independent files for Sparse Histogram\n- Implement classic methods of bias correction (see [8,9] for the definition of bias correction)\n- Quantile Mapping [5,7,14], parametric and non parametric version\n- CDFt methods [6] \n- OTC and dOTC methods [9]\n- R2D2 method [11]\n- MBCn method [4]\n- QDM method [3]\n- MRec method [1]\n- ECBC method [12]\n- TSMBC method [15], for autocorrelations.\n\n## How to select a bias correction method ?\n\nThis summary of ability of each method to perform a bias correction is proposed by Fran\u00e7ois, (2020). Please refer to\nthis article for further interpretation.\n\n| Characteristics | CDF-t | R2D2 | dOTC | MBCn | MRec |\n|---------------------------------------------| :----------------: | :----------------: | :----------------: | :----------------: | :----------------: |\n| Correction of univariate dist. prop. | Yes | Yes | Yes | Yes | Yes |\n| Modification of correlations of the model | No | Yes | Yes | Yes | Yes |\n| Capacity to correct inter-var. prop. | No | Yes | Yes | Yes | Yes |\n| Capacity to correct spatial prop. | No | Yes | Yes | ~ | ~ |\n| Capacity to correct temporal prop. | No | No | No | No | No |\n| Preserve the rank structure of the model | Yes | ~ | ~ | ~ | ~ |\n| Capacity to correct small geographical area | n.a. | Yes | Yes | Yes | Yes |\n| Capacity to correct large geographical area | n.a. | ~ | ~ | ~ | No |\n| Allow for change of the multi-dim. prop. | Yes | No | Yes | ~ | Yes |\n\n\n## Python instruction\n\nRequires:\n- python3\n- [Eigen](http://eigen.tuxfamily.org/index.php?title=Main_Page)\n- numpy\n- scipy\n- pybind11\n\nYou can install from pypi:\n```\npip3 install SBCK\n```\n\nOr from sources:\n```\ngit clone https://github.com/yrobink/SBCK-python.git\ncd SBCK\npip3 install .\n```\n\nIf the Eigen library is not found, use:\n```\npip3 install . eigen=\"path-to-eigen\"\n```\n\n## Acknowledgements\n\nThanks to [[Trevor James Smith]](https://github.com/Zeitsperre) for his help with the publication on pypi.\n\n\n## License\n\nCopyright(c) 2021 / 2023 Yoann Robin\n\nThis file is part of SBCK.\n\nSBCK is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or\n(at your option) any later version.\n\nSBCK is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. 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Climate, 28, 6938\u20136959, https://doi.org/10.1175/JCLI-D-14-00754.1, 2015.\n- [[4]](https://link.springer.com/article/10.1007/s00382-017-3580-6) Cannon, Alex J.: Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables, Climate Dynamics, nb. 1, vol. 50, p. 31-49, 10.1007/s00382-017-3580-6, 2018.\n- [[5]](https://doi.org/10.1016/j.gloplacha.2006.11.030) D\u00e9qu\u00e9, M.: Frequency of precipitation and temperature extremes over France in an anthropogenic scenario: Model results and statistical correction according to observed values, Global Planet. Change, 57, 16\u201326, https://doi.org/10.1016/j.gloplacha.2006.11.030, 2007.\n- [[6]](https://doi.org/10.1029/2009GL038401) Michelangeli, P.-A., Vrac, M., and Loukos, H.: Probabilistic downscaling approaches: Application to wind cumulative distribution functions, Geophys. Res. Lett., 36, L11708, https://doi.org/10.1029/2009GL038401, 2009.\n- [7] Panofsky, H. A. and Brier, G. W.: Some applications of statistics to meteorology, Mineral Industries Extension Services, College of Mineral Industries, Pennsylvania State University, 103 pp., 1958.\n- [[8]](https://doi.org/10.1016/j.jhydrol.2010.10.024) Piani, C., Weedon, G., Best, M., Gomes, S., Viterbo, P., Hagemann, S., and Haerter, J.: Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models, J. Hydrol., 395, 199\u2013215, https://doi.org/10.1016/j.jhydrol.2010.10.024, 2010.\n- [[9]](https://doi.org/10.5194/hess-23-773-2019) Robin, Y., Vrac, M., Naveau, P., Yiou, P.: Multivariate stochastic bias corrections with optimal transport, Hydrol. Earth Syst. 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