A comparison of three predictor selection methods for statistical downscaling
Three predictor selection methods [correlation analysis, partial correlation analysis and stepwise regression analysis (SRA)] that are commonly used for statistical downscaling are compared in terms of the uncertainty assessments of their downscaled results using the same statistical downscaling mod...
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| Autor*in: |
Yang, Chunli [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Rechteinformationen: |
Nutzungsrecht: © 2016 Royal Meteorological Society |
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| Schlagwörter: |
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| Systematik: |
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| Übergeordnetes Werk: |
Enthalten in: International journal of climatology - Chichester [u.a.] : Wiley, 1989, 37(2017), 3, Seite 1238-1249 |
|---|---|
| Übergeordnetes Werk: |
volume:37 ; year:2017 ; number:3 ; pages:1238-1249 |
| Links: |
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| DOI / URN: |
10.1002/joc.4772 |
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| 520 | |a Three predictor selection methods [correlation analysis, partial correlation analysis and stepwise regression analysis (SRA)] that are commonly used for statistical downscaling are compared in terms of the uncertainty assessments of their downscaled results using the same statistical downscaling model (SDSM). Uncertainty is assessed by comparing several statistical indices for observed and downscaled daily precipitation, daily maximum and minimum temperature, monthly means and variances of daily precipitation and daily temperature. Besides these, the distributions of monthly mean of daily precipitation, monthly dry and wet days also are considered. The analysis employs the SDSM and 54 years (1961–2014) of observed daily precipitation and temperature together with National Center for Environmental Prediction ( NCEP ) reanalysis predictors. A comparison of the different methods for selecting predictors indicates that SRA is slight better than other two methods in most statistical indices. | ||
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10.1002/joc.4772 doi PQ20170501 (DE-627)OLC1992739986 (DE-599)GBVOLC1992739986 (PRQ)p1842-2a311a9e67a0db3c00e7f4592f0983b367dff0af46a228b339aced30d3d8824b3 (KEY)0104704320170000037000301238comparisonofthreepredictorselectionmethodsforstati DE-627 ger DE-627 rakwb eng 550 DNB RA 1000 AVZ rvk Yang, Chunli verfasserin aut A comparison of three predictor selection methods for statistical downscaling 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Three predictor selection methods [correlation analysis, partial correlation analysis and stepwise regression analysis (SRA)] that are commonly used for statistical downscaling are compared in terms of the uncertainty assessments of their downscaled results using the same statistical downscaling model (SDSM). Uncertainty is assessed by comparing several statistical indices for observed and downscaled daily precipitation, daily maximum and minimum temperature, monthly means and variances of daily precipitation and daily temperature. Besides these, the distributions of monthly mean of daily precipitation, monthly dry and wet days also are considered. The analysis employs the SDSM and 54 years (1961–2014) of observed daily precipitation and temperature together with National Center for Environmental Prediction ( NCEP ) reanalysis predictors. A comparison of the different methods for selecting predictors indicates that SRA is slight better than other two methods in most statistical indices. Nutzungsrecht: © 2016 Royal Meteorological Society uncertainty assessment SDSM predictor selection methods Correlation analysis Wang, Ninglian oth Wang, Shijin oth Enthalten in International journal of climatology Chichester [u.a.] : Wiley, 1989 37(2017), 3, Seite 1238-1249 (DE-627)130763128 (DE-600)1000947-4 (DE-576)023035773 0899-8418 nnns volume:37 year:2017 number:3 pages:1238-1249 http://dx.doi.org/10.1002/joc.4772 Volltext http://onlinelibrary.wiley.com/doi/10.1002/joc.4772/abstract http://search.proquest.com/docview/1873484631 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_22 GBV_ILN_4311 RA 1000 AR 37 2017 3 1238-1249 |
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10.1002/joc.4772 doi PQ20170501 (DE-627)OLC1992739986 (DE-599)GBVOLC1992739986 (PRQ)p1842-2a311a9e67a0db3c00e7f4592f0983b367dff0af46a228b339aced30d3d8824b3 (KEY)0104704320170000037000301238comparisonofthreepredictorselectionmethodsforstati DE-627 ger DE-627 rakwb eng 550 DNB RA 1000 AVZ rvk Yang, Chunli verfasserin aut A comparison of three predictor selection methods for statistical downscaling 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Three predictor selection methods [correlation analysis, partial correlation analysis and stepwise regression analysis (SRA)] that are commonly used for statistical downscaling are compared in terms of the uncertainty assessments of their downscaled results using the same statistical downscaling model (SDSM). Uncertainty is assessed by comparing several statistical indices for observed and downscaled daily precipitation, daily maximum and minimum temperature, monthly means and variances of daily precipitation and daily temperature. Besides these, the distributions of monthly mean of daily precipitation, monthly dry and wet days also are considered. The analysis employs the SDSM and 54 years (1961–2014) of observed daily precipitation and temperature together with National Center for Environmental Prediction ( NCEP ) reanalysis predictors. A comparison of the different methods for selecting predictors indicates that SRA is slight better than other two methods in most statistical indices. Nutzungsrecht: © 2016 Royal Meteorological Society uncertainty assessment SDSM predictor selection methods Correlation analysis Wang, Ninglian oth Wang, Shijin oth Enthalten in International journal of climatology Chichester [u.a.] : Wiley, 1989 37(2017), 3, Seite 1238-1249 (DE-627)130763128 (DE-600)1000947-4 (DE-576)023035773 0899-8418 nnns volume:37 year:2017 number:3 pages:1238-1249 http://dx.doi.org/10.1002/joc.4772 Volltext http://onlinelibrary.wiley.com/doi/10.1002/joc.4772/abstract http://search.proquest.com/docview/1873484631 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_22 GBV_ILN_4311 RA 1000 AR 37 2017 3 1238-1249 |
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A comparison of three predictor selection methods for statistical downscaling |
| abstract |
Three predictor selection methods [correlation analysis, partial correlation analysis and stepwise regression analysis (SRA)] that are commonly used for statistical downscaling are compared in terms of the uncertainty assessments of their downscaled results using the same statistical downscaling model (SDSM). Uncertainty is assessed by comparing several statistical indices for observed and downscaled daily precipitation, daily maximum and minimum temperature, monthly means and variances of daily precipitation and daily temperature. Besides these, the distributions of monthly mean of daily precipitation, monthly dry and wet days also are considered. The analysis employs the SDSM and 54 years (1961–2014) of observed daily precipitation and temperature together with National Center for Environmental Prediction ( NCEP ) reanalysis predictors. A comparison of the different methods for selecting predictors indicates that SRA is slight better than other two methods in most statistical indices. |
| abstractGer |
Three predictor selection methods [correlation analysis, partial correlation analysis and stepwise regression analysis (SRA)] that are commonly used for statistical downscaling are compared in terms of the uncertainty assessments of their downscaled results using the same statistical downscaling model (SDSM). Uncertainty is assessed by comparing several statistical indices for observed and downscaled daily precipitation, daily maximum and minimum temperature, monthly means and variances of daily precipitation and daily temperature. Besides these, the distributions of monthly mean of daily precipitation, monthly dry and wet days also are considered. The analysis employs the SDSM and 54 years (1961–2014) of observed daily precipitation and temperature together with National Center for Environmental Prediction ( NCEP ) reanalysis predictors. A comparison of the different methods for selecting predictors indicates that SRA is slight better than other two methods in most statistical indices. |
| abstract_unstemmed |
Three predictor selection methods [correlation analysis, partial correlation analysis and stepwise regression analysis (SRA)] that are commonly used for statistical downscaling are compared in terms of the uncertainty assessments of their downscaled results using the same statistical downscaling model (SDSM). Uncertainty is assessed by comparing several statistical indices for observed and downscaled daily precipitation, daily maximum and minimum temperature, monthly means and variances of daily precipitation and daily temperature. Besides these, the distributions of monthly mean of daily precipitation, monthly dry and wet days also are considered. The analysis employs the SDSM and 54 years (1961–2014) of observed daily precipitation and temperature together with National Center for Environmental Prediction ( NCEP ) reanalysis predictors. A comparison of the different methods for selecting predictors indicates that SRA is slight better than other two methods in most statistical indices. |
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| title_short |
A comparison of three predictor selection methods for statistical downscaling |
| url |
http://dx.doi.org/10.1002/joc.4772 http://onlinelibrary.wiley.com/doi/10.1002/joc.4772/abstract http://search.proquest.com/docview/1873484631 |
| remote_bool |
false |
| author2 |
Wang, Ninglian Wang, Shijin |
| author2Str |
Wang, Ninglian Wang, Shijin |
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130763128 |
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| doi_str |
10.1002/joc.4772 |
| up_date |
2024-07-04T05:34:23.237Z |
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1803625444921049088 |
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