Research Commons
      • Browse 
        • Communities & Collections
        • Titles
        • Authors
        • By Issue Date
        • Subjects
        • Types
        • Series
      • Help 
        • About
        • Collection Policy
        • OA Mandate Guidelines
        • Guidelines FAQ
        • Contact Us
      • My Account 
        • Sign In
        • Register
      View Item 
      •   Research Commons
      • University of Waikato Research
      • Science and Engineering
      • The International Global Change Institute (IGCI)
      • International Global Change Institute Papers
      • View Item
      •   Research Commons
      • University of Waikato Research
      • Science and Engineering
      • The International Global Change Institute (IGCI)
      • International Global Change Institute Papers
      • View Item
      JavaScript is disabled for your browser. Some features of this site may not work without it.

      Statistical downscaling of regional daily precipitation over southeast Australia based on self-organizing maps

      Yin, Chonghua; Li, Yinpeng; Ye, Wei; Bornman, Janet F.; Yan, Xiaodong
      DOI
       10.1007/s00704-010-0371-y
      Link
       www.springerlink.com
      Find in your library  
      Citation
      Export citation
      Yin, C., Li, Y., Ye, W., Bornman, J.F. & Yan, X. (2010). Statistical downscaling of regional daily precipitation over southeast Australia based on self-organizing maps. Theoretical and Applied Climatology, available online on 21 November 2010.
      Permanent Research Commons link: https://hdl.handle.net/10289/4850
      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 was constructed 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 by downscaling 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 was no 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 observed precipitation, 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.
      Date
      2010
      Type
      Journal Article
      Publisher
      Springer
      Collections
      • International Global Change Institute Papers [13]
      Show full item record  

      Usage

       
       
       

      Usage Statistics

      For this itemFor all of Research Commons

      The University of Waikato - Te Whare Wānanga o WaikatoFeedback and RequestsCopyright and Legal Statement