- Title
- Statistical downscaling of general circulation model outputs to precipitation—part 1: calibration and validation
- Creator
- Sachindra, Dhanapala; Huang, Fuchun; Barton, Andrew; Perera, Bimalka
- Date
- 2014
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/167884
- Identifier
- vital:13748
- Identifier
-
https://doi.org/10.1002/joc.3914
- Identifier
- ISBN:0899-8418
- Abstract
- This article is the first of two companion articles providing details of the development of two separate models for statistically downscaling monthly precipitation. The first model was developed with National Centers for Environmental Prediction/National Center for Atmospheric Research (/) reanalysis outputs and the second model was built using the outputs of Hadley Centre Coupled Model version 3 (). Both models were based on the multi‐linear regression () technique and were built for a precipitation station located in Victoria, Australia. Probable predictors were selected based on the past literature and hydrology. Potential predictors were selected for each calendar month separately from the / reanalysis data, considering the correlations that they maintained with observed precipitation. Based on the strength of the correlations, these potential predictors were introduced to the downscaling model until its performance in validation, in terms of Nash–Sutcliffe Efficiency (), was maximized. In this manner, for each calendar month, the final sets of potential predictors and the best downscaling models with / reanalysis data were identified. The 20th century climate experiment data corresponding to these final sets of potential predictors were used to calibrate and validate the second model. In calibration and validation, the model developed with / reanalysis data displayed of 0.74 and 0.70, respectively. The model built with outputs showed of 0.44 and 0.17 during the calibration and validation periods, respectively. Both models tended to under‐predict high precipitation values and over‐predict near‐zero precipitation values, during both calibration and validation. However, this prediction characteristic was more pronounced by the model developed with outputs. A graphical comparison of observed precipitation, the precipitation reproduced by the two downscaling models and the raw precipitation output of , showed that there is large bias in the precipitation output of . This indicated the need of a bias‐correction, which is detailed in the second companion article.
- Relation
- International Journal of Climatology Vol. 34, no. 11 (2014), p. 3264-3281
- Rights
- https://creativecommons.org/licenses/by/3.0/au/deed.en
- Rights
- © 2014 The Authors. International Journal of Climatology published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
- Rights
- This metadata is freely available under a CCO license
- Subject
- Statistical Downscaling; Precipitation; General Circulation Model; 0401 Atmospheric Sciences; 0905 Civil Engineering; 0907 Environmental Engineering
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