A framework for estimating cloudy sky surface downward longwave radiation from the derived active and passive cloud property parameters
A framework for estimating cloudy sky surface downward longwave radiation from the derived active and passive cloud property parameters
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根据导出的主动和被动云属性参数估计多云天空表面向下长波辐射的框架
DOI：
10.1016/j.rse.2020.111972
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发表时间：
2020-10
影响因子：
13.5
通讯作者：
Cheng Jie
中科院分区：
文献类型：
--
作者：
Yang Feng;Cheng Jie
云底温度(CBT)是影响多云天空下行长波辐射(SDLR)的主要参数之一。然而，CBT在区域和全球尺度上的应用很少，在估计云天空SDLR方面的应用也是有限的。在这项研究中，提出了一种在全球范围内估计白天和夜间云天空SDLR的框架。这一框架由三部分组成。首先，将从主动云卫星数据提取的云垂直结构(CVS)参数与从被动MODIS数据提取的云特征参数相结合，构建全球云属性数据库。其次，建立了ISCCP云分类系统和MODIS云分类系统下估算云厚度(CT)的经验方法。此外，利用所构建的云属性数据库对CERES CT估计模型的系数进行了修正。有了估计的CT和再分析数据，计算CBT就很简单了。将估计的ISCCP云型CT的精确度与在地方尺度上进行的现有研究进行了比较。我们的CT估计精度可与现有研究相媲美。根据ARM、NSA和SGP站的验证结果，用改进的CT模式估算的MODIS云型的CT比用原CERES CT模式估算的CT要好。最后，通过将估计的CBT和其他参数反馈到单层云模式(SLCM)，得到了多云天空SDLR值。经SURFRAD网络的地面测量SDLR值验证后，其偏差和均方根误差分别为5.42W∙m−2和30.3W∙m−2。这一准确性与主流SDLR产品的评估结果相当(Gui等人)。2010)，SLCMS的新评价结果(Yu et al.2018)，以及一种新的多云天空SDLR估计方法的准确性(Wang等人2018年)。与直接使用云顶温度(CTT)的SLCM相比，所有推导出的CBT都能更好地提高SDLR的估计精度。我们将收集更多的地面测量数据，并在未来继续验证所开发的框架。
The cloud-base temperature (CBT) is one of the parameters that dominates the cloudy sky surface downward longwave radiation (SDLR). However, CBT is rarely available at regional and global scales, and its application in estimating cloud sky SDLR is limited. In this study, a framework to globally estimate cloud sky SDLR during both daytime and nighttime is proposed. This framework is composed of three parts. First, a global cloudy property database was constructed by combing the extracted cloud vertical structure (CVS) parameters from the active CloudSat data and cloud properties from passive MODIS data. Second, the empirical methods for estimating cloud thickness (CT) under ISCCP cloud classification system and MODIS cloud classification system were developed. Additionally, the coefficients of CERES CT estimate models were refitted using the constructed cloud property database. With the estimated CT and reanalysis data, calculating the CBT is straightforward. The accuracy of the estimated CT for ISCCP cloud type is compared with the existing studies that were conducted at local scales. Our CT estimate accuracy is comparable to that of the existing studies. According to the validation results at ARM NSA and SGP stations, the CT estimated by the developed CT model for MODIS cloud type is better than that estimated by the original CERES CT model. Finally, the cloudy sky SDLR values were derived by feeding the estimated CBT and other parameters to the single-layer cloud model (SLCM). When validated by the ground measured SDLR collected from the SURFRAD network, the bias and RMSE are 5.42 W∙m−2and 30.3 W∙m−2, respectively. This accuracy is comparable to the evaluation results of the mainstream SDLR products (Gui et al. 2010), the new evaluation results of SLCMs (Yu et al. 2018), and the accuracy of a new cloudy sky SDLR estimate method (Wang et al. 2018). All the derived CBTs improve the SDLR estimate accuracy more than the SLCM that directly uses cloud-top temperature (CTT). We will collect more ground measurements and continue to validate the developed framework in the future.
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影响因子：
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