Statistical Downscaling of Temperature with the Random Forest Model
Joint Authors
Xu, Zongxue
Pang, Bo
Yue, Jiajia
Zhao, Gang
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-06-15
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
The issues with downscaling the outputs of a global climate model (GCM) to a regional scale that are appropriate to hydrological impact studies are investigated using the random forest (RF) model, which has been shown to be superior for large dataset analysis and variable importance evaluation.
The RF is proposed for downscaling daily mean temperature in the Pearl River basin in southern China.
Four downscaling models were developed and validated by using the observed temperature series from 61 national stations and large-scale predictor variables derived from the National Center for Environmental Prediction–National Center for Atmospheric Research reanalysis dataset.
The proposed RF downscaling model was compared to multiple linear regression, artificial neural network, and support vector machine models.
Principal component analysis (PCA) and partial correlation analysis (PAR) were used in the predictor selection for the other models for a comprehensive study.
It was shown that the model efficiency of the RF model was higher than that of the other models according to five selected criteria.
By evaluating the predictor importance, the RF could choose the best predictor combination without using PCA and PAR.
The results indicate that the RF is a feasible tool for the statistical downscaling of temperature.
American Psychological Association (APA)
Pang, Bo& Yue, Jiajia& Zhao, Gang& Xu, Zongxue. 2017. Statistical Downscaling of Temperature with the Random Forest Model. Advances in Meteorology،Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1122887
Modern Language Association (MLA)
Pang, Bo…[et al.]. Statistical Downscaling of Temperature with the Random Forest Model. Advances in Meteorology No. 2017 (2017), pp.1-11.
https://search.emarefa.net/detail/BIM-1122887
American Medical Association (AMA)
Pang, Bo& Yue, Jiajia& Zhao, Gang& Xu, Zongxue. Statistical Downscaling of Temperature with the Random Forest Model. Advances in Meteorology. 2017. Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1122887
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1122887