Relationship between sunshine duration and solar radiation

Suehrcke, H.; Bowden, R.S.; Hollands, K.G.T.

doi:10.1016/j.solener.2013.02.026

Cited by 75 publications

(37 citation statements)

References 16 publications

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“…The performance of sunshine duration based radiation estimation R s is the mainly limitation of photosynthesis in subtropical area of China, where is characterized by wet and warm summer and dry and mild winter with complex aerosolcloud-precipitation interactions. The heavy clouds, aerosol and frequent rainy days considerably influence the radiation estimation quality as the sunshine recorder is insensitive to the low radiation and the actual threshold depends on the humidity of the recording card (Besharat et al, 2013;Suehrcke et al, 2013). Previous study also found that various radiation products including reanalysis data (MERRA, ECMWF, NCEP) and satellite-derived products (GLASS, ISCCP) showed low correlation and large product errors in south China, resulting in large uncertainties in the GPP simulations driven by those data .…”

Section: Discussionmentioning

confidence: 99%

“…Although a logistic relationship between diffuse radiation fraction and clearness index could be established, R se led to the R 2 and RMSE of the estimated R d from 0.90 and 1.07 MJ m À2 day À1 to 0.67 and 1.94 MJ m À2 day À1 , implying that the quality of solar radiation had serious impact on R d estimation. The relationship between R d and sunshine duration can be directly established, and might improve the R d estimation (Suehrcke et al, 2013). However, this is out of our scope as many ecosystem models only take R s /R se as input data, and here we focused on the error propagation of R se to R d and GPP estimation.…”

Section: Discussionmentioning

confidence: 99%

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Impact of estimated solar radiation on gross primary productivity simulation in subtropical plantation in southeast China

et al. 2015

Solar Energy

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Sunshine duration is widely used to estimate solar radiation, but this estimated inherently contains some uncertainties, limiting its applications. This study investigated the impacts of the estimated solar radiation on simulated gross primary productivity (GPP), which were obtained using ecosystem models -light use efficiency model (LUE) and process-based model -Boreal Ecosystem Productivity Simulator (BEPS) at an evergreen coniferous forest ecosystem in southeast China. The models for solar radiation and diffuse radiation estimation were calibrated through observation data from nearby meteorological stations. The results showed that the established model could be successfully used to estimate solar radiation with high coefficient of determination (0.92) and low root mean square error (2.18 MJ m À2 day À1 ), but the solar radiation was overestimated when the clearness index was less than 0.15 and underestimated when it was within the range of 0.2-0.35 or greater than 0.6. The estimated solar radiation has significant influence on the diffuse radiation estimation and GPP simulation comparing with using observations. The two ecosystem models reacted differently to the errors of estimated solar radiation. For the LUE model, the estimated solar radiation led to the underestimated GPP in growing season (May-October), and overestimated GPP during non-growing season (November-April) with the bias ranged from À11% to 10% depending on the month of a year. For the BEPS model, estimated solar radiation resulted in overestimated GPP in most months with the bias ranged from À6% to 20%. The difference between the simulated GPP based on these two sources of solar radiation could be counteracted to some extent at the annual scale, especially for LUE model.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Impact of estimated solar radiation on gross primary productivity simulation in subtropical plantation in southeast China

et al. 2015

Solar Energy

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“…7. Herein, non-linear form drawn on the figure as daily relationship between sunshine duration and solar irradiation is similar to drawing obtained by Suehrcke et al (2013). After the estimation step, observed and estimated data are compared on the basis of errors.…”

Section: Anfis Modelmentioning

confidence: 96%

“…Here are a few data were faulty, hence these data have been eliminated from graphics. The reason is; faulty data can affect the structure of the new model (Suehrcke et al, 2013).…”

Section: Angströ M-prescott Modelmentioning

confidence: 99%

“…Furthermore Suehrcke (2000) suggests that the Angströ m equation coefficients a and b depend on the sunshine fraction. Suehrcke et al (2013) provides information on the analysis of the sunshine-radiation data with a physical reason for nonlinearity of the relationship. On the other hand, Samuel (1991) developed a cubic formulation for the solar irradiation potential estimation.…”

Section: Introductionmentioning

confidence: 99%

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Solar irradiation estimations and comparisons by ANFIS, Angström–Prescott and dependency models

et al. 2014

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Improved estimation of global solar radiation over rugged terrains by the disaggregation of Satellite Applications Facility on Land Surface Analysis data (LSA SAF)

Fibbi

Maselli

Pieri

2020

Meteorological Applications

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This paper presents a new method to predict global solar radiation over irregular terrain, named Estimation of global solar RADiation (ERAD). The method is based on the disaggregation of Satellite Applications Facility on Land Surface Analysis (LSA SAF) data using a digital elevation model and is applied in Italy with a time step of 1 min and a spatial resolution of 200 m. A quantitative assessment of ERAD is performed in comparison with three other standard methods (Mountain Microclimate Simulation Model [MTCLIM], LSA SAF and Copernicus Atmosphere Monitoring Service [CAMS]) using measurements taken in 43 stations located in Italy or in the surrounding countries, in the years 2005–2016. Such assessment concerns the irradiance incoming on a horizontal surface, which is measured by ground radiation sensors and is summarized by means of four accuracy statistics (i.e. mean absolute error [MAE], root mean square error [RMSE], coefficient of determination [R2] and mean bias error [MBE]). Overall, the average daily global solar radiation estimates obtained by ERAD have RMSE and R2 about 25 W·m−2 and 0.943, respectively. These statistics are similar to those of LSA SAF and better than those of CAMS and, above all, MTCLIM. The bias analysis by elevation ranges shows a slight ERAD overestimation over plains and hills and a slight underestimation over mountains. An additional qualitative assessment shows how the ERAD radiation estimates are more spatially detailed than those of the other methods and are redistributed on inclined surfaces consistently with expectations.

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Relationship between sunshine duration and solar radiation

Cited by 75 publications

References 16 publications

Impact of estimated solar radiation on gross primary productivity simulation in subtropical plantation in southeast China

Impact of estimated solar radiation on gross primary productivity simulation in subtropical plantation in southeast China

Solar irradiation estimations and comparisons by ANFIS, Angström–Prescott and dependency models

Improved estimation of global solar radiation over rugged terrains by the disaggregation of Satellite Applications Facility on Land Surface Analysis data (LSA SAF)

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