Two ultraviolet radiation datasets that cover China
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Ultraviolet (UV) radiation has significant effects on ecosystems, environments, and human health, as well as atmospheric processes and climate change. Two ultraviolet radiation datasets are described in this paper. One contains hourly observations of UV radiation measured at 40 Chinese Ecosystem Research Network stations from 2005 to 2015. CUV3 broadband radiometers were used to observe the UV radiation, with an accuracy of 5%, which meets the World Meteorology Organization’s measurement standards. The extremum method was used to control the quality of the measured datasets. The other dataset contains daily cumulative UV radiation estimates that were calculated using an all-sky estimation model combined with a hybrid model. The reconstructed daily UV radiation data span from 1961 to 2014. The mean absolute bias error and root-mean-square error are smaller than 30% at most stations, and most of the mean bias error values are negative, which indicates underestimation of the UV radiation intensity. These datasets can improve our basic knowledge of the spatial and temporal variations in UV radiation. Additionally, these datasets can be used in studies of potential ozone formation and atmospheric oxidation, as well as simulations of ecological processes.
Key wordsultraviolet radiation observation hybrid model reconstruction China
紫外辐射在生态系统, 大气环境, 人体健康以及气候学等研究领域有重要作用. 本论文描述了两个紫外辐射数据集. 一是中国生态系统研究网络(CERN)2005-2015年40个站点紫外辐射小时观测数据集. 对紫外辐射的观测采用CUV3 紫外辐射表, 不确定度为5%, 满足世界气象组织(WMO)的要求. 采用极值法对观测数据进行质量控制. 另一个数据集是紫外辐射日累计重构值, 由全天候紫外辐射估算公式结合混合模型的方法计算获得, 该数据集的时间长度为1961-2014年. 大多数站点紫外辐射重构模型的绝对偏差和均方根误差都小于30%, 且相对偏差为负, 表明该模型对紫外辐射的估算略偏低. 这两个紫外辐射数据集可以提高我们对紫外辐射时空变化特征的认识, 并且可用于研究臭氧生成潜势和大气氧化性以及模拟生态过程.
关键词紫外辐射 观测 混合模型 重构 中国
We thank CERN for providing the radiation observations. The global solar radiation data and meteorological elements used in this study were obtained from the CMA, which is highly appreciated by the authors. We also gratefully acknowledge the MODIS Science Team for providing the AOD dataset and the NASA/GSFC Ozone Processing Team for supplying the ozone data.
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