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Title: Validation and Spatiotemporal Analysis of CERES Surface Net Radiation Product

The Clouds and the Earth’s Radiant Energy System (CERES) generates one of the few global satellite radiation products. The CERES ARM Validation Experiment (CAVE) has been providing long-term in situ observations for the validation of the CERES products. However, the number of these sites is low and their distribution is globally sparse, and particularly the surface net radiation product has not been rigorously validated yet. Therefore, additional validation efforts are highly required to determine the accuracy of the CERES radiation products. In this study, global land surface measurements were comprehensively collected for use in the validation of the CERES net radiation (Rn) product on a daily (340 sites) and a monthly (260 sites) basis, respectively. The validation results demonstrated that the CERES Rn product was, overall, highly accurate. The daily validations had a Mean Bias Error (MBE) of 3.43 W·m−2, Root Mean Square Error (RMSE) of 33.56 W·m−2, and R2 of 0.79, and the monthly validations had an MBE of 3.40 W·m−2, RMSE of 25.57 W·m−2, and R2 of 0.84. The accuracy was slightly lower for the high latitudes. Following the validation, the monthly CERES Rn product, from March 2000 to July 2014, was used for a further analysis. Wemore » analysed the global spatiotemporal variation of the Rn, which occurred during the measurement period. In addition, two hot spot regions, the southern Great Plains and south-central Africa, were then selected for use in determining the driving factors or attribution of the Rn variation. We determined that Rn over the southern Great Plains decreased by −0.33 W·m−2 per year, which was mainly driven by changes in surface green vegetation and precipitation. In south-central Africa, Rn decreased at a rate of −0.63 W·m−2 per year, the major driving factor of which was surface green vegetation.« less
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  1. Beijing Normal Univ., Beijing (China). State Key Laboratory of Remote Sensing Science
  2. Beijing Normal Univ., Beijing (China). State Key Laboratory of Remote Sensing Science; Univ. of Maryland, College Park, MD (United States)
Publication Date:
OSTI Identifier:
Grant/Contract Number:
FG02-04ER63917; 41401381; 41101310
Accepted Manuscript
Journal Name:
Remote Sensing
Additional Journal Information:
Journal Volume: 8; Journal Issue: 2; Journal ID: ISSN 2072-4292
Research Org:
Univ. of Maryland, College Park, MD (United States)
Sponsoring Org:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
Country of Publication:
United States
54 ENVIRONMENTAL SCIENCES; CERES; net radiation; validation; spatiotemporal analysis; attribution