Strategies for Statistical‐Dynamical Downscaling to Urban Climate Using Global Data.
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| Title: | Strategies for Statistical‐Dynamical Downscaling to Urban Climate Using Global Data. |
|---|---|
| Authors: | Boettcher, Marita1 (AUTHOR) marita.boettcher@uni‐hamburg.de, Flagg, David D.2 (AUTHOR), Grawe, David1 (AUTHOR), Hoffmann, Peter3 (AUTHOR), Petrik, Ronny4 (AUTHOR), Schlünzen, K. Heinke1 (AUTHOR), Schoetter, Robert5 (AUTHOR) |
| Source: | International Journal of Climatology. Feb2026, Vol. 46 Issue 2, p1-18. 18p. |
| Subject Terms: | *Climate change models, *Urban climatology, Downscaling (Climatology), Numerical grid generation (Numerical analysis), Atmospheric models |
| Geographic Terms: | Germany, Hamburg (Germany) |
| Abstract: | Statistical‐dynamical downscaling is one method to model urban climate using global and regional climate model (GCM and RCM) results. In this study, different strategies for statistical‐dynamical downscaling are derived and evaluated. For the statistical part of downscaling, a Bivariate Skill Score is developed to quantify the overlap of the joint probability densities of two meteorological variables, which helps to quantify whether the statistically selected days are representative of the full climatology. Results show that for representing the winter climate of the selected urban area (Hamburg, Germany), more days need to be simulated than for summer climate (129 days vs. 40 days simulated). For the dynamical part of the downscaling (from ~20 km to 250 m), the mesoscale atmospheric model METRAS is used with two different grid structures: (a) downscaling with three one‐way nested domains and (b) downscaling with one domain employing a non‐uniform grid. The downscaling method with the non‐uniform grid is less expensive in preparing and computing resources than the method with tree one‐way nests. The evaluation shows that similar model results are achieved in both cases in the domain of interest. The evaluation of temperature, relative humidity, wind speed and wind direction with different metrics shows that METRAS performs well for summer and winter climate, and slightly better for summer. Furthermore, METRAS performs well with both downscaling methods and the evaluation measures are slightly better for the downscaling with the non‐uniform grid. For further downscaling of GCM or RCM results to a very local scale, for example, with obstacle resolving models, using a non‐uniform grid may be a good solution to reduce the number of necessary downscaling steps and the related work load for computer and humans. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Climatology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | GreenFILE |
| FullText | Text: Availability: 0 |
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| Header | DbId: 8gh DbLabel: GreenFILE An: 191577166 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Strategies for Statistical‐Dynamical Downscaling to Urban Climate Using Global Data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Boettcher%2C+Marita%22">Boettcher, Marita</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> marita.boettcher@uni‐hamburg.de</i><br /><searchLink fieldCode="AR" term="%22Flagg%2C+David+D%2E%22">Flagg, David D.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Grawe%2C+David%22">Grawe, David</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hoffmann%2C+Peter%22">Hoffmann, Peter</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Petrik%2C+Ronny%22">Petrik, Ronny</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Schlünzen%2C+K%2E+Heinke%22">Schlünzen, K. Heinke</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Schoetter%2C+Robert%22">Schoetter, Robert</searchLink><relatesTo>5</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Climatology%22">International Journal of Climatology</searchLink>. Feb2026, Vol. 46 Issue 2, p1-18. 18p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Climate+change+models%22">Climate change models</searchLink><br />*<searchLink fieldCode="DE" term="%22Urban+climatology%22">Urban climatology</searchLink><br /><searchLink fieldCode="DE" term="%22Downscaling+%28Climatology%29%22">Downscaling (Climatology)</searchLink><br /><searchLink fieldCode="DE" term="%22Numerical+grid+generation+%28Numerical+analysis%29%22">Numerical grid generation (Numerical analysis)</searchLink><br /><searchLink fieldCode="DE" term="%22Atmospheric+models%22">Atmospheric models</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Germany%22">Germany</searchLink><br /><searchLink fieldCode="DE" term="%22Hamburg+%28Germany%29%22">Hamburg (Germany)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Statistical‐dynamical downscaling is one method to model urban climate using global and regional climate model (GCM and RCM) results. In this study, different strategies for statistical‐dynamical downscaling are derived and evaluated. For the statistical part of downscaling, a Bivariate Skill Score is developed to quantify the overlap of the joint probability densities of two meteorological variables, which helps to quantify whether the statistically selected days are representative of the full climatology. Results show that for representing the winter climate of the selected urban area (Hamburg, Germany), more days need to be simulated than for summer climate (129 days vs. 40 days simulated). For the dynamical part of the downscaling (from ~20 km to 250 m), the mesoscale atmospheric model METRAS is used with two different grid structures: (a) downscaling with three one‐way nested domains and (b) downscaling with one domain employing a non‐uniform grid. The downscaling method with the non‐uniform grid is less expensive in preparing and computing resources than the method with tree one‐way nests. The evaluation shows that similar model results are achieved in both cases in the domain of interest. The evaluation of temperature, relative humidity, wind speed and wind direction with different metrics shows that METRAS performs well for summer and winter climate, and slightly better for summer. Furthermore, METRAS performs well with both downscaling methods and the evaluation measures are slightly better for the downscaling with the non‐uniform grid. For further downscaling of GCM or RCM results to a very local scale, for example, with obstacle resolving models, using a non‐uniform grid may be a good solution to reduce the number of necessary downscaling steps and the related work load for computer and humans. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Climatology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/joc.70180 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 1 Subjects: – SubjectFull: Climate change models Type: general – SubjectFull: Urban climatology Type: general – SubjectFull: Downscaling (Climatology) Type: general – SubjectFull: Numerical grid generation (Numerical analysis) Type: general – SubjectFull: Atmospheric models Type: general – SubjectFull: Germany Type: general – SubjectFull: Hamburg (Germany) Type: general Titles: – TitleFull: Strategies for Statistical‐Dynamical Downscaling to Urban Climate Using Global Data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Boettcher, Marita – PersonEntity: Name: NameFull: Flagg, David D. – PersonEntity: Name: NameFull: Grawe, David – PersonEntity: Name: NameFull: Hoffmann, Peter – PersonEntity: Name: NameFull: Petrik, Ronny – PersonEntity: Name: NameFull: Schlünzen, K. Heinke – PersonEntity: Name: NameFull: Schoetter, Robert IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 08998418 Numbering: – Type: volume Value: 46 – Type: issue Value: 2 Titles: – TitleFull: International Journal of Climatology Type: main |
| ResultId | 1 |
