Estimation of downwelling surface longwave radiation for cloudy skies by considering the radiation effect from the entire cloud layers
文献类型：期刊论文
| 作者 | Jiang, Yun4; Tang, Bo-Hui; Zhang, Huanyu4 |
| 刊名 | REMOTE SENSING OF ENVIRONMENT
 |
| 出版日期 | 2023-12-01 |
| 卷号 | 298页码:113829 |
| 关键词 | Downwelling surface longwave radiation (DSLR) Cloudy sky Cloud base height (CBH) Cloud base temperature (CBT) Cloud top height |
| DOI | 10.1016/j.rse.2023.113829 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Cloud base height (CBH) is a key parameter to characterize the cloud radiation effect. However, the CBH used in downwelling surface longwave radiation (DSLR) estimation is generally obtained indirectly through cloud top parameters retrieved by passive optical remote sensing instruments, which is of high uncertainty. At the same time, it is unreasonable to replace the effective radiation of the entire cloud layers by only using the cloud base radiation with single layer cloud model. This study proposes a new method to estimate cloudy-sky DSLR, which considers the radiation effect of the entire cloud layers from the cloud base to top. First, the CBH estimation model is established by the genetic algorithm-artificial neural network (GA-ANN) algorithm. The cloud top height and cloud attribute parameters (cloud optical depth, cloud water path, and cloud phase) from the passive remote sensing are used as the input features, and meanwhile CBH data from the active remote sensing are output features in the training and testing process of the model. Then, the cloud base temperature (CBT) is estimated based on the CBH combined with the temperature profile data in the EAR5 reanalysis data. Finally, the effective temperature of the entire cloud layers is calculated by using CBT and cloud top temperature. The verification results of CBH estimation showed that R2 is 0.83, the bias and root mean square error (RMSE) are 0.02 km and 1.56 km, respectively, which indicates a comparable accuracy and higher stability compared with the previous studies. The ground-based measurements in the SURFRAD network are used to validate the newly proposed DSLR estimation method, and the results showed that the bias and RMSE are 5.27 W/m2 and 28.48 W/ m2, respectively. Additionally, this study found that although the effective temperature of the entire cloud layers has a weaker linear correlation with DSLR, the radiation contribution generated by cloud still occupies a certain weight, and the maximum ratio of cloud radiation in DSLR estimation can account for 30%. Therefore, the cloud radiation effect must be taken into account in the estimation of cloudy-sky DSLR. |
| WOS关键词 | DOWNWARD RADIATION ; SATELLITE ; PARAMETERIZATION ; ALGORITHM ; MODIS ; CLEAR ; FLUX |
| WOS研究方向 | Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS记录号 | WOS:001097558600001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/199439]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Tang, Bo-Hui |
| 作者单位 | 1.Yunnan Prov Dept Educ, Key Lab Plateau Remote Sensing, Kunming 650093, Peoples R China 2.Kunming Univ Sci & Technol, Fac Land Resource Engn, Kunming 650093, Peoples R China 3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Jiang, Yun,Tang, Bo-Hui,Zhang, Huanyu. Estimation of downwelling surface longwave radiation for cloudy skies by considering the radiation effect from the entire cloud layers[J]. REMOTE SENSING OF ENVIRONMENT,2023,298:113829. |
| APA | Jiang, Yun,Tang, Bo-Hui,&Zhang, Huanyu.(2023).Estimation of downwelling surface longwave radiation for cloudy skies by considering the radiation effect from the entire cloud layers.REMOTE SENSING OF ENVIRONMENT,298,113829. |
| MLA | Jiang, Yun,et al."Estimation of downwelling surface longwave radiation for cloudy skies by considering the radiation effect from the entire cloud layers".REMOTE SENSING OF ENVIRONMENT 298(2023):113829. |
入库方式： OAI收割
来源：地理科学与资源研究所
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