Precipitation Bias Correction: A Novel Semi-parametric Quantile Mapping Method
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F23%3A97245" target="_blank" >RIV/60460709:41330/23:97245 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1029/2023EA002823" target="_blank" >http://dx.doi.org/10.1029/2023EA002823</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1029/2023EA002823" target="_blank" >10.1029/2023EA002823</a>
Alternative languages
Result language
angličtina
Original language name
Precipitation Bias Correction: A Novel Semi-parametric Quantile Mapping Method
Original language description
Bias correction methods are used to adjust simulations from global and regional climate models to use them in informed decision-making. Here we introduce a semi-parametric quantile mapping (SPQM) method to bias-correct daily precipitation. This method uses a parametric probability distribution to describe observations and an empirical distribution for simulations. Bias-correction techniques typically adjust the bias between observation and historical simulations to correct projections. The SPQM however corrects simulations based only on observations assuming the detrended simulations have the same distribution as the observations. Thus, the bias-corrected simulations preserve the climate change signal, including changes in the magnitude and probability dry, and guarantee a smooth transition from observations to future simulations. The results are compared with popular quantile mapping techniques, that is, the quantile delta mapping (QDM) and the statistical transformation of the CDF using splines (SSPLINE). The SPQM performed well in reproducing the observed statistics, marginal distribution, and wet and dry spells. Comparatively, it performed at least equally well as the QDM and SSPLINE, specifically in reproducing observed wet spells and extreme quantiles. The method is further tested in a basin-scale region. The spatial variability and statistics of the observed precipitation are reproduced well in the bias-corrected simulations. Overall, the SPQM is easy to apply, yet robust in bias-correcting daily precipitation simulations.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10511 - Environmental sciences (social aspects to be 5.7)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Earth and Space Science
ISSN
2333-5084
e-ISSN
2333-5084
Volume of the periodical
10
Issue of the periodical within the volume
4
Country of publishing house
US - UNITED STATES
Number of pages
17
Pages from-to
1-17
UT code for WoS article
000978224200001
EID of the result in the Scopus database
2-s2.0-85153791014