Extreme rainfall events have been great interest in statistical downscaling. This paper concerns with developing model of statistical downscaling using quantile regression to estimate extreme monthly rainfall. Statistical downscaling relates functionally local scale response variable and global scale predictor variables. The response variable is monthly rainfall from 1979 to 2008 at station Bangkir Indonesia and the predictor variables are monthly precipitation of 64 grid of Global Circulation Model output in the same period. Principal Component Analysis is used to reduce dimension of predictors. A number of components for developing quantile regression model are determined based on Quantile Verification Skill Score. The results show that at 95th quantile the pattern of forecasted rainfall in January to December 2008 is similar to actual rainfall with correlation 0.98 and the forecasted rainfall (843 mm) in February 2008 is considered as the extreme rainfall which confirms well to the highest actual rainfall (727 mm) with probability 0.99.
| Published in | Science Journal of Applied Mathematics and Statistics (Volume 2, Issue 3) |
| DOI | 10.11648/j.sjams.20140203.12 |
| Page(s) | 66-70 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Collinear, Extreme, Principal Component Analysis, Statistical Downscaling, Quantile Regression, Logistic Regression
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| [9] | S. Chamaille´-Jammesa, H. Fritz, and F. Murindagomo. Detecting climate changes of concern in highly variable environments : Quantile regressions reveal that droughts worsen in Hwange National Park, Zimbabwe. Journal of Arid Environments, 71, 2007, pp. 21–326. |
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APA Style
Aji Hamim Wigena, Anik Djuraidah. (2014). Quantile Regression in Statistical Downscaling to Estimate Extreme Monthly Rainfall. Science Journal of Applied Mathematics and Statistics, 2(3), 66-70. https://doi.org/10.11648/j.sjams.20140203.12
ACS Style
Aji Hamim Wigena; Anik Djuraidah. Quantile Regression in Statistical Downscaling to Estimate Extreme Monthly Rainfall. Sci. J. Appl. Math. Stat. 2014, 2(3), 66-70. doi: 10.11648/j.sjams.20140203.12
AMA Style
Aji Hamim Wigena, Anik Djuraidah. Quantile Regression in Statistical Downscaling to Estimate Extreme Monthly Rainfall. Sci J Appl Math Stat. 2014;2(3):66-70. doi: 10.11648/j.sjams.20140203.12
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Download@article{10.11648/j.sjams.20140203.12,
author = {Aji Hamim Wigena and Anik Djuraidah},
title = {Quantile Regression in Statistical Downscaling to Estimate Extreme Monthly Rainfall},
journal = {Science Journal of Applied Mathematics and Statistics},
volume = {2},
number = {3},
pages = {66-70},
doi = {10.11648/j.sjams.20140203.12},
url = {https://doi.org/10.11648/j.sjams.20140203.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20140203.12},
abstract = {Extreme rainfall events have been great interest in statistical downscaling. This paper concerns with developing model of statistical downscaling using quantile regression to estimate extreme monthly rainfall. Statistical downscaling relates functionally local scale response variable and global scale predictor variables. The response variable is monthly rainfall from 1979 to 2008 at station Bangkir Indonesia and the predictor variables are monthly precipitation of 64 grid of Global Circulation Model output in the same period. Principal Component Analysis is used to reduce dimension of predictors. A number of components for developing quantile regression model are determined based on Quantile Verification Skill Score. The results show that at 95th quantile the pattern of forecasted rainfall in January to December 2008 is similar to actual rainfall with correlation 0.98 and the forecasted rainfall (843 mm) in February 2008 is considered as the extreme rainfall which confirms well to the highest actual rainfall (727 mm) with probability 0.99.},
year = {2014}
}
TY - JOUR T1 - Quantile Regression in Statistical Downscaling to Estimate Extreme Monthly Rainfall AU - Aji Hamim Wigena AU - Anik Djuraidah Y1 - 2014/07/20 PY - 2014 N1 - https://doi.org/10.11648/j.sjams.20140203.12 DO - 10.11648/j.sjams.20140203.12 T2 - Science Journal of Applied Mathematics and Statistics JF - Science Journal of Applied Mathematics and Statistics JO - Science Journal of Applied Mathematics and Statistics SP - 66 EP - 70 PB - Science Publishing Group SN - 2376-9513 UR - https://doi.org/10.11648/j.sjams.20140203.12 AB - Extreme rainfall events have been great interest in statistical downscaling. This paper concerns with developing model of statistical downscaling using quantile regression to estimate extreme monthly rainfall. Statistical downscaling relates functionally local scale response variable and global scale predictor variables. The response variable is monthly rainfall from 1979 to 2008 at station Bangkir Indonesia and the predictor variables are monthly precipitation of 64 grid of Global Circulation Model output in the same period. Principal Component Analysis is used to reduce dimension of predictors. A number of components for developing quantile regression model are determined based on Quantile Verification Skill Score. The results show that at 95th quantile the pattern of forecasted rainfall in January to December 2008 is similar to actual rainfall with correlation 0.98 and the forecasted rainfall (843 mm) in February 2008 is considered as the extreme rainfall which confirms well to the highest actual rainfall (727 mm) with probability 0.99. VL - 2 IS - 3 ER -
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