The Weather Generator Used in the Empirical Statistical Downscaling Method, WETTREG
In this paper, the weather generator (WG) used by the empirical statistical downscaling method, weather situation-based regionalization method (in German: WETTerlagen-basierte REGionalisierungsmethode, WETTREG), is described. It belongs to the class of multi-site parametric models that aim at the re...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2013-06-01
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| Series: | Atmosphere |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2073-4433/4/2/169 |
| _version_ | 1818564244461846528 |
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| author | Wolfgang Enke Arne Spekat Frank Kreienkamp |
| author_facet | Wolfgang Enke Arne Spekat Frank Kreienkamp |
| author_sort | Wolfgang Enke |
| collection | DOAJ |
| description | In this paper, the weather generator (WG) used by the empirical statistical downscaling method, weather situation-based regionalization method (in German: WETTerlagen-basierte REGionalisierungsmethode, WETTREG), is described. It belongs to the class of multi-site parametric models that aim at the representation of the spatial dependence among weather variables with conditioning on exogenous atmospheric predictors. The development of the WETTREG WG was motivated by (i) the requirement of climate impact modelers to obtain input data sets that are consistent and can be produced in a relatively economic way and (ii) the well-sustained hypothesis that large scale atmospheric features are well reproduced by climate models and can be used as a link to regional climate. The WG operates at daily temporal resolution. The conditioning factor is the temporal development of the frequency distribution of circulation patterns. Following a brief description of the strategy of classifying circulation patterns that have a strong link to regional climate, the bulk of this paper is devoted to a description of the WG itself. This includes aspects, such as the utilized building blocks, seasonality or the methodology with which a signature of climate change is imprinted onto the generated time series. Further attention is given to particularities of the WG’s conditioning processes, as well as to extremes, areal representativity and the interface of WGs and user requirements. |
| first_indexed | 2024-12-14T01:26:35Z |
| format | Article |
| id | doaj.art-f9205700ec4c4971a74d2160dbfc415b |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-12-14T01:26:35Z |
| publishDate | 2013-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-f9205700ec4c4971a74d2160dbfc415b2022-12-21T23:22:10ZengMDPI AGAtmosphere2073-44332013-06-014216919710.3390/atmos4020169The Weather Generator Used in the Empirical Statistical Downscaling Method, WETTREGWolfgang EnkeArne SpekatFrank KreienkampIn this paper, the weather generator (WG) used by the empirical statistical downscaling method, weather situation-based regionalization method (in German: WETTerlagen-basierte REGionalisierungsmethode, WETTREG), is described. It belongs to the class of multi-site parametric models that aim at the representation of the spatial dependence among weather variables with conditioning on exogenous atmospheric predictors. The development of the WETTREG WG was motivated by (i) the requirement of climate impact modelers to obtain input data sets that are consistent and can be produced in a relatively economic way and (ii) the well-sustained hypothesis that large scale atmospheric features are well reproduced by climate models and can be used as a link to regional climate. The WG operates at daily temporal resolution. The conditioning factor is the temporal development of the frequency distribution of circulation patterns. Following a brief description of the strategy of classifying circulation patterns that have a strong link to regional climate, the bulk of this paper is devoted to a description of the WG itself. This includes aspects, such as the utilized building blocks, seasonality or the methodology with which a signature of climate change is imprinted onto the generated time series. Further attention is given to particularities of the WG’s conditioning processes, as well as to extremes, areal representativity and the interface of WGs and user requirements.http://www.mdpi.com/2073-4433/4/2/169statistical climatologyclimate modelingweather generatorempirical statistical downscalingweather patternsenvironment-to-circulation |
| spellingShingle | Wolfgang Enke Arne Spekat Frank Kreienkamp The Weather Generator Used in the Empirical Statistical Downscaling Method, WETTREG Atmosphere statistical climatology climate modeling weather generator empirical statistical downscaling weather patterns environment-to-circulation |
| title | The Weather Generator Used in the Empirical Statistical Downscaling Method, WETTREG |
| title_full | The Weather Generator Used in the Empirical Statistical Downscaling Method, WETTREG |
| title_fullStr | The Weather Generator Used in the Empirical Statistical Downscaling Method, WETTREG |
| title_full_unstemmed | The Weather Generator Used in the Empirical Statistical Downscaling Method, WETTREG |
| title_short | The Weather Generator Used in the Empirical Statistical Downscaling Method, WETTREG |
| title_sort | weather generator used in the empirical statistical downscaling method wettreg |
| topic | statistical climatology climate modeling weather generator empirical statistical downscaling weather patterns environment-to-circulation |
| url | http://www.mdpi.com/2073-4433/4/2/169 |
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