摘要
Although solar radiation is a crucial parameter in designing solar power devices and studying land surface processes,long-term and densely distributed observations of surface solar radiation are usually not available.This paper describes the development of a 50-year dataset of daily surface solar radiation at 716 China Meteorological Administration(CMA) stations.First,a physical model,without any local calibration,is applied to estimate the daily radiation at all 716 CMA routine stations.Then,an ANN-based(Artificial Neural Network) model is applied to extend radiation estimates to earlier periods at each of all 96 CMA radiation stations.The ANN-based model is trained with recent reliable radiation data and thus its estimate is more reliable than the physical model.Therefore,the ANN-based model is used to correct the physical model dynamically at a monthly scale.The correction generally improves the accuracy of the radiation dataset estimated by the physical model:the mean bias error(MBE) averaged over all the 96 radiation stations during 1994-2002 is reduced from 0.68 to 0.11 MJ m-2 and the root mean square error(RMSE) from 2.01 to 1.80 MJ m-2.The new radiation dataset shows superior performance over previous estimates by locally calibrated ngstr m-Prescott models.Based on the new radiation dataset,the annual mean daily solar radiation over China is 14.3 MJ m-2.The maximal seasonal mean daily solar radiation occurs in the Tibetan Plateau during summer with a value of 27.1 MJ m-2,whereas the minimal seasonal mean daily solar radiation occurs in the Sichuan Basin during winter with a value of 4.7 MJ m-2.
Although solar radiation is a crucial parameter in designing solar power devices and studying land surface processes,long-term and densely distributed observations of surface solar radiation are usually not available.This paper describes the development of a 50-year dataset of daily surface solar radiation at 716 China Meteorological Administration（CMA） stations.First,a physical model,without any local calibration,is applied to estimate the daily radiation at all 716 CMA routine stations.Then,an ANN-based（Artificial Neural Network） model is applied to extend radiation estimates to earlier periods at each of all 96 CMA radiation stations.The ANN-based model is trained with recent reliable radiation data and thus its estimate is more reliable than the physical model.Therefore,the ANN-based model is used to correct the physical model dynamically at a monthly scale.The correction generally improves the accuracy of the radiation dataset estimated by the physical model：the mean bias error（MBE） averaged over all the 96 radiation stations during 1994-2002 is reduced from 0.68 to 0.11 MJ m-2 and the root mean square error（RMSE） from 2.01 to 1.80 MJ m-2.The new radiation dataset shows superior performance over previous estimates by locally calibrated ngstr m-Prescott models.Based on the new radiation dataset,the annual mean daily solar radiation over China is 14.3 MJ m-2.The maximal seasonal mean daily solar radiation occurs in the Tibetan Plateau during summer with a value of 27.1 MJ m-2,whereas the minimal seasonal mean daily solar radiation occurs in the Sichuan Basin during winter with a value of 4.7 MJ m-2.
基金
supported by the Global Change Program of Ministry of Science and Technology of China(Grant No.2010CB951703)
China's "863"Project(Grant No.2009AA122100)
the "100-Talent" Program of Chinese Academy of Sciences
