Validation and Spatiotemporal Analysis of CERES Surface Net Radiation Product

Journal Article · Sat Jan 23 00:00:00 EST 2016 · Remote Sensing

DOI:https://doi.org/10.3390/rs8020090· OSTI ID:1254481

Jia, Aolin ^[1]; Jiang, Bo ^[1]; Liang, Shunlin ^[2]; Zhang, Xiaotong ^[1]; Ma, Han ^[1]

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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)

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 (R_n) product on a daily (340 sites) and a monthly (260 sites) basis, respectively. The validation results demonstrated that the CERES R_n 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 R_n product, from March 2000 to July 2014, was used for a further analysis. The global spatiotemporal variation of the R_n, which occurred during the measurement period, was analyzed. 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 R_n variation. We determined that R_n 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, R_n decreased at a rate of -0.63 W·m^-2 per year, the major driving factor of which was surface green vegetation

• Research Organization: Univ. of Maryland, College Park, MD (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Biological and Environmental Research (BER)
• Grant/Contract Number: FG02-04ER63917; 41401381; 41101310
• OSTI ID: 1254481
• Journal Information: Remote Sensing, Vol. 8, Issue 2; ISSN 2072-4292
• Publisher: MDPI
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES
CERES
net radiation
validation
spatiotemporal analysis
attribution