The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Bias Correction, Quantile Mapping, and Downscaling: Revisiting the Inflation Issue
Abstract
Quantile mapping is routinely applied to correct biases of regional climate model simulations compared to observational data. If the observations are of similar resolution as the regional climate model, quantile mapping is a feasible approach. However, if the observations are of much higher resolution, quantile mapping also attempts to bridge this scale mismatch. Here, it is shown for daily precipitation that such quantile mapping–based downscaling is not feasible but introduces similar problems as inflation of perfect prognosis (“prog”) downscaling: the spatial and temporal structure of the corrected time series is misrepresented, the drizzle effect for area means is overcorrected, area-mean extremes are overestimated, and trends are affected. To overcome these problems, stochastic bias correction is required.
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Citations
Cites
An Introduction to Statistical Modeling of Extreme Values
Coles, S. (2001) · Springer Series in Statistics · doi:10.1007/978-1-4471-3675-0 · Cited by 7,352
Christensen, J. H., Boberg, F., Christensen, O. B., & Lucas‐Picher, P. (2008) · Geophysical Research Letters · doi:10.1029/2008gl035694 · Cited by 729
Cited by
Cannon, A. J., Sobie, S. R., & Murdock, T. Q. (2015) · Journal of Climate · doi:10.1175/jcli-d-14-00754.1 · Cited by 1,700
A trend-preserving bias correction – the ISI-MIP approach
Hempel, S., Frieler, K., Warszawski, L., Schewe, J., & Piontek, F. (2013) · Earth System Dynamics · doi:10.5194/esd-4-219-2013 · Cited by 1,238
“For this central range bias correction methods are expected to perform well, while the correction in the outer ranges of the distribution is typically worse, since there are less events (Maraun, 2013).”
BackgroundImproved Bias Correction Techniques for Hydrological Simulations of Climate Change*
Pierce, D. W., Cayan, D. R., Maurer, E. P., Abatzoglou, J. T., & Hegewisch, K. C. (2015) · Journal of Hydrometeorology · doi:10.1175/jhm-d-14-0236.1 · Cited by 332
Liu, J. C., Wilson, A., Mickley, L. J., Dominici, F., Ebisu, K., Wang, Y., Sulprizio, M. P., Peng, R. D., Yue, X., Son, J.-Y., et al. (2017) · Epidemiology · doi:10.1097/ede.0000000000000556 · Cited by 303
Climate model biases in the eastern tropical oceans: causes, impacts and ways forward.
Richter, I. (2015) · WIREs Climate Change · doi:10.1002/wcc.338 · Cited by 253
Metadata
- Authors
- Maraun, D.
- Venue
- Journal of Climate
- Publisher
- American Meteorological Society
- Published
- 2013-03-15
- Volume
- 26
- Issue
- 6
- Pages
- 2137-2143
- ISSN
- 0894-8755 (ISSN-L)
- 1520-0442
- OpenAlex
- W2171254377
- Cited by
- 741
- References
- 2
- FWCI
- 23.42
- Type
- journal article
- Language
- en
- Open access
- closed
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- Maraun, D. (2013). Bias Correction, Quantile Mapping, and Downscaling: Revisiting the Inflation Issue. Journal of Climate. https://doi.org/10.1175/jcli-d-12-00821.1
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