Articles | Volume 21, issue 3
https://doi.org/10.5194/hess-21-1693-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-21-1693-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
A combined statistical bias correction and stochastic downscaling method for precipitation
Claudia Volosciuk
CORRESPONDING AUTHOR
GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany
Douglas Maraun
Wegener Center for Climate and Global Change, University of Graz, Graz, Austria
Mathieu Vrac
Laboratoire des Sciences du Climat et de l'Environnement (LSCE), CNRS/IPSL, Gif-sur-Yvette, France
Martin Widmann
School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK
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- Bias Correction of Surface Air Temperature and Precipitation in Cordex East Asia Simulation: What Should We Do When Applying Bias Correction? J. Chen et al. 10.2139/ssrn.4092894
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- Study of climate change impact on hydro-climatic extremes in the Hanjiang River basin, China, using CORDEX-EAS data C. Dai et al. 10.1016/j.wace.2022.100509
- On the Joint Calibration of Multivariate Seasonal Climate Forecasts from GCMs A. Schepen et al. 10.1175/MWR-D-19-0046.1
- Probabilistic Simulationof Big Climate Data for Robust Quantification of Changes in Compound Hazard Events T. Economou & F. Garry 10.2139/ssrn.4106219
- Atmospheric River Sequences as Indicators of Hydrologic Hazard in Historical Reanalysis and GFDL SPEAR Future Climate Projections C. Bowers et al. 10.1029/2023EF003536
- Time Scale Decomposition of Climate and Correction of Variability Using Synthetic Samples of Stable Distributions M. Gomez‐Garcia et al. 10.1029/2018WR023053
- Regional modeling of daily precipitation fields across the Great Lakes region (Canada) using the CFSR reanalysis D. Khedhaouiria et al. 10.1007/s00477-019-01722-x
- Spatio-temporal variation of precipitation projection based on bias-adjusted CORDEX-SA regional climate model simulations for arid and semi-arid region A. Ghaemi et al. 10.3354/cr01722
- A Novel Spatial Downscaling Approach for Climate Change Assessment in Regions With Sparse Ground Data Networks Y. Kim et al. 10.1029/2021GL095729
- Uncertainty quantification in intensity-duration-frequency curves under climate change: Implications for flood-prone tropical cities W. Zhao et al. 10.1016/j.atmosres.2022.106070
- The stationarity of two statistical downscaling methods for precipitation under different choices of cross‐validation periods Y. Wang et al. 10.1002/joc.5375
- Revisiting the bias correction of climate models for impact studies T. Dinh & F. Aires 10.1007/s10584-023-03597-y
Latest update: 23 Nov 2024
Short summary
For impact modeling, infrastructure design, or adaptation strategy planning, high-quality climate data on the point scale are often demanded. Due to the scale gap between gridbox and point scale and biases in climate models, we combine a statistical bias correction and a stochastic downscaling model and apply it to climate model-simulated precipitation. The method performs better in summer than in winter and in winter best for mild winter climate (Mediterranean) and worst for continental winter.
For impact modeling, infrastructure design, or adaptation strategy planning, high-quality...