Empirical estimation of daytime net radiation from shortwave radiation and ancillary information

Jiang, Bo; Zhang, Yi; Liang, Shunlin; Wohlfahrt, Georg; Arain, M. Altaf; Cescatti, Alessandro; Georgiadis, T.; Jia, Kun; Kiely, Gerard; Lund, Magnus; Montagnani, Leonardo; Magliulo, Vincenzo; Serrano-Ortiz, Penélope; Oechel, Walter C.; Vaccari, Francesco Primo; Yao, Yunjun; Zhang, Xiaolin

doi:10.1016/j.agrformet.2015.05.003

Cited by 53 publications

(46 citation statements)

References 46 publications

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“…Remote sensing retrieval has become one of the most important methods for obtaining the radiation data [4]. However, clouds make it impossible to retrieve surface thermal radiation components directly from satellite observations, and the alternative solution is to estimate the all-wave net radiation from the satellite shortwave net radiation product, in conjunction with other information [5,6].…”

Section: Introductionmentioning

confidence: 99%

Validation and Spatiotemporal Analysis of CERES Surface Net Radiation Product

et al. 2016

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Abstract: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 R 2 of 0.79, and the monthly validations had an MBE of 3.40 W¨m´2, RMSE of 25.57 W¨m´2, and R 2 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.

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Section: Introductionmentioning

confidence: 99%

Validation and Spatiotemporal Analysis of CERES Surface Net Radiation Product

et al. 2016

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“…The robustness of the two models in global mode was examined by comparing the prediction accuracies of four categories, with the results shown in Table 6 and Figure 4 (Figure 4a-d, 4i-l). Generally speaking, the GRNN global model worked much better and more stable than Neuroet, especially for S2 and S3, which were difficult to represent by empirical linear models [26]. It is noteworthy that the prediction for S3 by the two models was also quite well if the RMSEs and biases were examined rather than R2, which indicates that all points were relatively clustered (Figure 4c,g,k,o).…”

Section: Comparison Of the Two Ann Modelsmentioning

confidence: 83%

“…The fitting results will be discussed for each category. Though these measurements were quality controlled by the networks, they were further examined manually by us, and then all measurements were averaged to reasonable daytime values as proposed by Jiang et al [26]. Note that daytime was defined as the period between sunrise and sunset at each site.…”

Section: In-situ Datamentioning

confidence: 99%

“…The elevation of these sites ranges from −0.7 m to 5063 m above sea level. To achieve a better understanding of the applicability of the two ANN models, four categories were established according to Jiang et al [26]. Based on extensive experiments, Jiang et al [26] found that NDVI = 0.2 can be used as the threshold to identify vegetated surfaces, and three more classes can be roughly divided based on albedo according to the different relations between Rsi and Rn when NDVI < 0.2 as shown in Table 3.…”

Section: In-situ Datamentioning

confidence: 99%

“…Some of these models focused on deriving Rn from satellite data for developing a continuous tempo-spatial regional or global Rn [22][23][24][25]. Recently, a study conducted by Jiang et al [26] compared and evaluated seven popular empirical linear models and one newly developed model for Rn estimation using comprehensive global measurements. The results indicated that the linear empirical models performed well, but not well enough in all cases, and therefore the author suggested that nonlinear empirical models should be considered for Rn estimation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Surface Daytime Net Radiation Estimation Using Artificial Neural Networks

et al. 2014

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Net all-wave surface radiation (Rn) is one of the most important fundamental parameters in various applications. However, conventional Rn measurements are difficult to collect because of the high cost and ongoing maintenance of recording instruments. Therefore, various empirical Rn estimation models have been developed. This study presents the results of two artificial neural network (ANN) models (general regression neural networks (GRNN) and Neuroet) to estimate Rn globally from multi-source data, including remotely sensed products, surface measurements, and meteorological reanalysis products. Rn estimates provided by the two ANNs were tested against in-situ radiation measurements obtained from 251 global sites between 1991-2010 both in global mode (all data were used to fit the models) and in conditional mode (the data were divided into four subsets and the models were fitted separately). Based on the results obtained from extensive experiments, it has been proved that the two ANNs were superior to linear-based empirical models in both global and conditional modes and that the GRNN performed better and was more stable than Neuroet. The GRNN estimates had a determination coefficient (R

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Comprehensive Assessment of Global Surface Net Radiation Products and Uncertainty Analysis

et al. 2018

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Earth surface net radiation (Rn) characterizes the surface radiation budget and plays a critical role in ecological, biogeochemical, and hydrological processes. The Rn products from remote sensing and reanalysis have not been validated comprehensively. In this study, four Rn 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 Rn 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 Rn anomalies over the global land surface. Detailed uncertainty analysis indicates that the global CERES Rn 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.

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Empirical estimation of daytime net radiation from shortwave radiation and ancillary information

Cited by 53 publications

References 46 publications

Validation and Spatiotemporal Analysis of CERES Surface Net Radiation Product

Validation and Spatiotemporal Analysis of CERES Surface Net Radiation Product

Surface Daytime Net Radiation Estimation Using Artificial Neural Networks

Comprehensive Assessment of Global Surface Net Radiation Products and Uncertainty Analysis

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