Applications of Self-Organizing Maps to Statistical Downscaling of Major Regional Climate Variables

TL;DRAbstract

This research developed a practical methodological framework, which integrated most of the important aspects related to statistical downscaling. The framework showed high skills when applied to downscale daily precipitation, minimum and maximum temperatures over southeast Australia. Within the framework, self-organizing maps (SOM) algorithm was incorporated as the core technique for interpreting the relationship between the predictor and predictand under consideration following the latest advances in synoptic climatology. The SOM classified large-scale predictors into a small number of synoptic patterns on a physically meaningful basis. By mapping the observed local climate variable (predictand) to these patterns, a downscaling model structure, SOM-SD, was constructed based on the NCAR/NCEP reanalysis data. Moreover, for a new atmospheric state, an ensemble of predictand values was generated by a stochastic re-sampling technique inside the SOM-SD. To improve seasonality of downscaled r

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