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http://dx.doi.org/10.7465/jkdi.2016.27.5.1215

Generating high resolution of daily mean temperature using statistical models  

Yoon, Sanghoo (Department of Statistics and Computer Science, Daegu University)
Publication Information
Journal of the Korean Data and Information Science Society / v.27, no.5, 2016 , pp. 1215-1224 More about this Journal
Abstract
Climate information of the high resolution grid units is an important factor to explain the phenomenon in a variety of research field. Statistical linear interpolation models are computationally inexpensive and applicable to any climate data compared to the dynamic simulation method at regional scales. In this paper, we considered four different linear-based statistical interpolation models: general linear model, generalized additive model, spatial linear regression model, and Bayesian spatial linear regression model. The climate variable of interest was the daily mean temperature, where the spatial variability was explained using geographic terrain information: latitude, longitude, elevation. The data were collected by weather stations in January from 2003 and 2012. In the sense of RMSE and correlation coefficient, Bayesian spatial linear regression model showed better performance in reflecting the spatial pattern compared to the other models.
Keywords
Cross-validation; generalized additive model; spatial linear regression model; statistical interpolation;
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Times Cited By KSCI : 7  (Citation Analysis)
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