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Abstract
Model output from climate projections often requires bias-adjustment to compensate for systematic model errors. A bias-adjustment method for extreme precipitation intensity is proposed that preserves the scaling equation for different accumulation levels from hourly to daily, using intensity-duration-frequency (IDF) modeling. A validation is performed within a pseudo-reality setting, based on hourly precipitation from 28 regional climate model projections of the EURO-CORDEX ensemble over Belgium. The scaling-based adjustment methods improve upon previous methods, an optimal method is identified, and, analytical quantile mapping methods must be avoided due to three identified problems. The ensemble mean of the adjusted extreme precipitation intensity obeys the above-mentioned scale-invariance property, which is consistent with observed extreme intensities. We thus show that IDF modeling provides added value in the context of bias-adjustment, and, that the particular IDF model proposed balances well between accuracy and the preservation of desired properties such as scale invariance and consistency among rainfall durations.
Original language | English |
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Article number | e2022EA002798 |
Pages (from-to) | 1-13 |
Number of pages | 13 |
Journal | earth and space science |
Volume | 10 |
Issue number | 3 |
DOIs | |
Publication status | Published - Mar 2023 |
Bibliographical note
Funding Information:This work is supported by URCLIM and has received funding from EU's H2020 Research and Innovation Program under Grant Agreement 690462. B.V.S. acknowledges support by the Belgian Science Policy (BELSPO) within the REGE+ (B2/212/P1/REGE+) project. L.D.C. acknowledges support from the Belgian Science Policy Office (BELSPO) through the FED-tWIN programme (Prf-2020-017). We acknowledge the World Climate Research Programme's Working Group on Regional Climate, and the Working Group on Coupled Modeling, former coordinating body of CORDEX and responsible panel for CMIP5. We also thank the climate modeling groups (listed in Table S1 in Supporting Information\u00A0S1) for producing and making available their model output. We also acknowledge the Earth System Grid Federation infrastructure, an international effort led by the U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison, the European Network for Earth System Modeling and other partners in the Global Organisation for Earth System Science Portals (GO-ESSP).
Publisher Copyright:
© 2023 The Authors. Earth and Space Science published by Wiley Periodicals LLC on behalf of American Geophysical Union.
Keywords
- bias correction
- climate change
- climate model ensemble
- IDF-models
- regional climate models
- subdaily precipitation extremes
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FOD139: Deep learning based Extreme Rainfall and flood warnIngs through Seamless foreCasting
1/09/22 → 1/12/26
Project: Fundamental
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OZR3893: Deep learning for quality control of crowdsourced data and seamless weather forecasting.
1/02/22 → 31/01/26
Project: Fundamental