Statistical downscaling of regional precipitation and temperature over southeast Australia based on self-organising maps
This paper presents a novel statistical downscaling method based on a non-linear classification technique known as self-organizing maps (SOMs) and has therefore been named SOM-SD. The relationship between large-scale atmospheric circulation and local-scale surface variable wasconstructed in a relati...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Published: |
Springer Wien
2011
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| Online Access: | http://hdl.handle.net/20.500.11937/15340 |
| _version_ | 1848748866908717056 |
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| author | Yin, C. Li, Y. Ye, W. Bornman, Janet Yan, X. |
| author_facet | Yin, C. Li, Y. Ye, W. Bornman, Janet Yan, X. |
| author_sort | Yin, C. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper presents a novel statistical downscaling method based on a non-linear classification technique known as self-organizing maps (SOMs) and has therefore been named SOM-SD. The relationship between large-scale atmospheric circulation and local-scale surface variable wasconstructed in a relatively simple and transparent manner. For a specific atmospheric state, an ensemble of possible values was generated for the predictand following the Monte Carlo method. Such a stochastic simulation is essential to explore the uncertainties of climate change in the future through a series of random re-sampling experiments. The novel downscaling method was evaluated bydownscaling daily precipitation over Southeast Australia. The large-scale predictors were extracted from the daily NCAR/NCEP reanalysis data, while the predictand was high resolution gridded daily observed precipitation (1958–2008) from the Australian Bureau of Meteorology. The results showed that the method works reasonably well across a variety of climatic zones in the study area. Overall, there wasno particular zone that stands out as a climatic entity where the downscaling skill in reproducing all statistical indices was consistently lower or higher across seasons than the other zones. The method displayed a high skill in reproducing not only the climatologic statistical properties of the observedprecipitation, but also the characteristics of the extreme precipitation events. Furthermore, the model was able to reproduce, to a certain extent, the inter-annual variability of precipitation characteristics. |
| first_indexed | 2025-11-14T07:11:51Z |
| format | Journal Article |
| id | curtin-20.500.11937-15340 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:11:51Z |
| publishDate | 2011 |
| publisher | Springer Wien |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-153402017-02-28T01:27:07Z Statistical downscaling of regional precipitation and temperature over southeast Australia based on self-organising maps Yin, C. Li, Y. Ye, W. Bornman, Janet Yan, X. This paper presents a novel statistical downscaling method based on a non-linear classification technique known as self-organizing maps (SOMs) and has therefore been named SOM-SD. The relationship between large-scale atmospheric circulation and local-scale surface variable wasconstructed in a relatively simple and transparent manner. For a specific atmospheric state, an ensemble of possible values was generated for the predictand following the Monte Carlo method. Such a stochastic simulation is essential to explore the uncertainties of climate change in the future through a series of random re-sampling experiments. The novel downscaling method was evaluated bydownscaling daily precipitation over Southeast Australia. The large-scale predictors were extracted from the daily NCAR/NCEP reanalysis data, while the predictand was high resolution gridded daily observed precipitation (1958–2008) from the Australian Bureau of Meteorology. The results showed that the method works reasonably well across a variety of climatic zones in the study area. Overall, there wasno particular zone that stands out as a climatic entity where the downscaling skill in reproducing all statistical indices was consistently lower or higher across seasons than the other zones. The method displayed a high skill in reproducing not only the climatologic statistical properties of the observedprecipitation, but also the characteristics of the extreme precipitation events. Furthermore, the model was able to reproduce, to a certain extent, the inter-annual variability of precipitation characteristics. 2011 Journal Article http://hdl.handle.net/20.500.11937/15340 Springer Wien restricted |
| spellingShingle | Yin, C. Li, Y. Ye, W. Bornman, Janet Yan, X. Statistical downscaling of regional precipitation and temperature over southeast Australia based on self-organising maps |
| title | Statistical downscaling of regional precipitation and temperature over southeast Australia based on self-organising maps |
| title_full | Statistical downscaling of regional precipitation and temperature over southeast Australia based on self-organising maps |
| title_fullStr | Statistical downscaling of regional precipitation and temperature over southeast Australia based on self-organising maps |
| title_full_unstemmed | Statistical downscaling of regional precipitation and temperature over southeast Australia based on self-organising maps |
| title_short | Statistical downscaling of regional precipitation and temperature over southeast Australia based on self-organising maps |
| title_sort | statistical downscaling of regional precipitation and temperature over southeast australia based on self-organising maps |
| url | http://hdl.handle.net/20.500.11937/15340 |