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    Statistical downscaling of regional precipitation and temperature over southeast Australia based on self-organising maps

    Access Status
    Fulltext not available
    Authors
    Yin, C.
    Li, Y.
    Ye, W.
    Bornman, Janet
    Yan, X.
    Date
    2011
    Type
    Journal Article
    
    Metadata
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    Citation
    Yin, C. and Li, Y. and Ye, W. and Bornman, J. and Yan, X. 2011. Statistical downscaling of regional precipitation and temperature over southeast Australia based on self-organising maps. Theoretical and Applied Climatology. 105 (1): pp. 11-26.
    Source Title
    Theoretical and Applied Climatology
    ISSN
    1434-4483
    URI
    http://hdl.handle.net/20.500.11937/15340
    Collection
    • Curtin Research Publications
    Abstract

    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.

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