
Determining the Parameters of the Ångström‐Prescott Model for Estimating Solar Radiation in Different Regions of China: Calibration and Modeling
Author(s) -
Liu Yujie,
Tan Qinghua,
Pan Tao
Publication year - 2019
Publication title -
earth and space science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.843
H-Index - 23
ISSN - 2333-5084
DOI - 10.1029/2019ea000635
Subject(s) - angstrom , calibration , environmental science , altitude (triangle) , china , meteorology , statistics , atmospheric sciences , climatology , mathematics , geology , geography , chemistry , geometry , archaeology , crystallography
The Ångström‐Prescott model (referred to as the A‐P model) is one of the most accurate and widely used models for estimating global solar radiation (Rs). In the absence of Rs measurements and given the regional discrepancy of model parameters, it is crucial to increase the availability of these parameters and the applicability of parameter‐predicted models in different regions. In this study, we evaluated and compared the applicability and performance of the calibrated model and eight predictive models in terms of A‐P model parameters, using daily Rs and meteorological data from 105 radiation stations in seven natural geographic zones in China. These models were evaluated based on their coefficient of determination ( R 2 ), root mean square error, Nash‐Sutcliffe efficiency coefficients, percent bias, and global performance indexes. Results indicated that altitude was the main factor determining the Ångström‐Prescott parameters in most regions. All models performed well, with acceptable accuracy across the whole country; however, their performances varied among regions. The best performing predictive models for the northeast region (Zone 1), north China (Zone 2), central China (Zone 3), south China (Zone 4), Inner Mongolia (Zone 5), northwest region (Zone 6), and Qinghai‐Tibet region (Zone 7) were obtained: These were Models 6, 1, 7, 3, 6, 1, and 7, respectively. The present results support the application of these predictive models for the estimation of daily global Rs in the corresponding regions of China, where measured Rs data are not available, and possibly in other regions with a similar climate.