贾傲林等：Comprehensive Assessment of Global Surface Net Radiation Products and Uncertainty Analysis

Comprehensive Assessment of Global Surface Net Radiation Products and Uncertainty Analysis

作者:Jia, AL (Jia, Aolin)[ 1 ] ; Liang, SL (Liang, Shunlin)[ 2 ] ; Jiang, B (Jiang, Bo)[ 1 ] ; Zhang, XT (Zhang, Xiaotong)[ 1 ] ; Wang, GX (Wang, Guoxin)[ 1 ]

JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES

卷: 123  期: 4  页: 1970-1989

DOI: 10.1002/2017JD027903

出版年: FEB 27 2018

文献类型:Article

摘要

Earth surface net radiation (R-n) characterizes the surface radiation budget and plays a critical role in ecological, biogeochemical, and hydrological processes. The R-n products from remote sensing and reanalysis have not been validated comprehensively. In this study, four R-n products (Clouds and the Earth's Radiant Energy System [CERES], ERA-Interim, Modern-Era Retrospective analysis for Research and Applications version 2, and Japanese 55-year Reanalysis) were validated using global ground measurements on monthly (255 sites) and annual (172 sites) timescales. These products have similar accuracies, with average root-mean-square error (RMSE) ranges of 5.35 W m(-2) (monthly) and 2.30 W m(-2) (annually). However, varying accuracies and systemic biases exist across different climatic zones. The annual land R-n intercomparison illustrates that large uncertainty exists over polar regions and deserts. A significantly negative annual anomaly in the CERES product for the 2001-2008 period is identified when examining annual R-n anomalies over the global land surface. Detailed uncertainty analysis indicates that the global CERES R-n anomaly is mainly due to different versions of input data such as aerosol optical thickness and atmospheric profiles (in 2006 and 2008) and cloud properties (in 2002). This work demonstrates that temporal analysis provides powerful quality control for global time series satellite products when the validation using ground measurements fails to capture potential issues.

通讯作者地址: Liang, SL (通讯作者)

Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA.

地址:

[ 1 ] Beijing Normal Univ, Fac Geog Sci, State Key Lab Remote Sensing Sci, Beijing, Peoples R China

[ 2 ] Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA