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  Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0)

Lange, S. (2019): Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0). - Geoscientific Model Development, 12, 7, 3055-3070.
https://doi.org/10.5194/gmd-12-3055-2019

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 ???ViewItemFull_lblCreators???:
Lange, Stefan1, ???ENUM_CREATORROLE_AUTHOR???           
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1Potsdam Institute for Climate Impact Research, ou_persistent13              

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 ???ViewItemFull_lblAbstract???: In this paper I present new methods for bias adjustment and statistical downscaling that are tailored to the requirements of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). In comparison to their predecessors, the new methods allow for a more robust bias adjustment of extreme values, preserve trends more accurately across quantiles, and facilitate a clearer separation of bias adjustment and statistical downscaling. The new statistical downscaling method is stochastic and better at adjusting spatial variability than the old interpolation method. Improvements in bias adjustment and trend preservation are demonstrated in a cross-validation framework.

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 ???ViewItemFull_lblDates???: 2019
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 ???ViewItemFull_lblIdentifiers???: ???ENUM_IDENTIFIERTYPE_DOI???: 10.5194/gmd-12-3055-2019
???ENUM_IDENTIFIERTYPE_PIKDOMAIN???: RD3 - Transformation Pathways
???ENUM_IDENTIFIERTYPE_EDOC???: 8552
???ENUM_IDENTIFIERTYPE_RESEARCHTK???: Climate impacts
???ENUM_IDENTIFIERTYPE_RESEARCHTK???: Extremes
???ENUM_IDENTIFIERTYPE_MODELMETHOD???: Machine Learning
???ENUM_IDENTIFIERTYPE_REGIONALK???: Global
???ENUM_IDENTIFIERTYPE_ORGANISATIONALK???: RD3 - Transformation Pathways
???ENUM_IDENTIFIERTYPE_WORKINGGROUP???: Data-Centric Modeling of Cross-Sectoral Impacts
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???ViewItemFull_lblSourceTitle???: Geoscientific Model Development
???ViewItemFull_lblSourceGenre???: ???ENUM_GENRE_JOURNAL???, SCI, Scopus, p3, oa
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