Estimation of daily and monthly direct, diffuse and global solar radiation from sunshine duration measurements

Benson, R.B.; Paris, M. V.; Sherry, J.E.; Justus, C. G.

doi:10.1016/0038-092x(84)90267-6

Cited by 135 publications

(47 citation statements)

References 32 publications

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“…The expected root-mean-square error associated withs measured with a Campbell-Stokes sunshine recorder is approximately 4.1% of the monthly percent possible sunshine (Benson et al, 1984). Measurement uncertainty in instantaneous pyranometric global irradiance measurements is ±5% (Myers et al, 1989).…”

Section: Error and Sensitivity Analysesmentioning

confidence: 98%

Comparison of aerosol and climate variability over Germany and South Africa

Power

Goyal

2003

Intl Journal of Climatology

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A radiation-based model is employed to evaluate seasonal, interannual, and spatial variability within monthly averaged turbidity (the total column amount of aerosol) at eight stations in South Africa and 13 stations in Germany. Germany has a strong seasonal turbidity trend of summer maxima and winter minima. In South Africa the seasonal turbidity trend is weaker, but, on average, turbidity peaks in spring and is lowest in autumn and winter. Over the last several decades, turbidity over Germany has decreased significantly at seven stations. The impacts of the El Chichón and Pinatubo volcanic eruptions are evident in the turbidity signatures over Germany. Four stations in South Africa show long-term trends in turbidity, but there is no consistent trend. Turbidity in Germany appears to be more influenced by aerosols of non-local origin than in South Africa, where the aerosol load may be of more local origin. Long-term monthly averages ofÅngström's turbidity coefficient β over Germany range between 0.019 and 0.143, and long-term annual averages range between 0.064 and 0.116. Over South Africa, turbidity is noticeably lower: long-term monthly averaged β ranges between 0.004 and 0.071, and the long-term annually averaged β ranges between 0.013 and 0.047. When averaged across all stations in each of the respective countries, turbidity is, on average, between 2.6 and 5.8 times higher in Germany than in South Africa, depending on the time of year.The climate of South Africa is clearly more humid than Germany, although both countries have strong seasonal trends in humidity. South Africa has become more humid over the last several decades. The decrease in turbidity at stations in Germany appears to be due to a reduction in aerosol emissions and/or changes in climatic factors other than humidity.

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Section: Error and Sensitivity Analysesmentioning

confidence: 98%

Comparison of aerosol and climate variability over Germany and South Africa

Power

Goyal

2003

Intl Journal of Climatology

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“…Appropriate shadowband correction factors were applied to the Kipp and Zonen diffuse radiation data and the uncertainty associated with both G m and D m is estimated to be less than 2% (Esterhuyse, personal communication, 2000). The Campbell-Stokes sunshine recorders have an expected root-mean-square error of approximately 4.1% of the monthly percentage of possible sunshine (Benson et al, 1984).…”

Section: Datamentioning

confidence: 99%

“…Löf et al, 1966;Rietveld, 1978;Hay, 1979;Iqbal, 1979;Benson et al, 1984;Ahmad et al, 1991;Hamdan and Al-Sayeh, 1991). Indeed, one of the most probable causes of trends in solar radiation is a change in the amount of cloud cover (Stanhill and Cohen, 2001).…”

Section: Sunshine Durationmentioning

confidence: 99%

Solar radiation climate change over southern Africa and an assessment of the radiative impact of volcanic eruptions

Power

Mills²

2005

Int. J. Climatol.

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Spatial and temporal variability in global, diffuse, and horizontal direct irradiance and sunshine duration has been evaluated at eight stations in South Africa and two stations in Namibia where the time series range between 21 and 41 years. Global and direct irradiance and sunshine duration decrease from northwest to southeast; diffuse irradiance increases toward the east. Annually averaged global irradiance G a decreased between 1.3% (2.8 W m −2 ) and 1.7% (4.4 W m −2 ) per decade at Bloemfontein, Cape Town, Durban, Pretoria, and Upington. Annually averaged diffuse irradiance D a decreased 5.2% (3.0 W m −2 ) per decade at Grootfontein and 4.2% (3.1 W m −2 ) per decade at Port Elizabeth. Annual direct irradiance B a decreased 2.1% (3.5 W m −2 ) per decade at Cape Town and 2.8% (5.7 W m −2 ) per decade at Alexander Bay. A simultaneous decrease in annually averaged daily sunshine duration S a may have contributed to the decrease in B a at Alexander Bay and the decrease in G a at Pretoria. Increases in aerosols may have contributed to the observed decrease in G a at Cape Town and Durban, and the decrease in D a at Grootfontein may be due to a decrease in aerosols. On average, variability in S a explains 89.0%, 50.4%, and 89.5% of the variance in G a , D a , and B a respectively. The radiative response to changes in sunshine duration is greater for direct irradiance than for global and diffuse. In the 2 years following the 1963 Mount Agung eruption in Indonesia, changes in global irradiance over southern Africa were small and inconsistent. At eight stations, diffuse irradiance increased 21.9% (13.3 W m −2 ) on average and direct irradiance decreased 8.7% (15.5 W m −2 ). After the 1982 El Chichón eruption in Mexico, global irradiance increased at two stations and decreased at seven stations. Eight stations witnessed an increase in diffuse irradiance averaging 7.2% (4.0 W m −2 ) and a decrease in direct irradiance of 5.0% (9.0 W m −2 ). The contribution of changes in cloud cover to the observed changes in irradiances appears to be small. Following the 1991 Mount Pinatubo eruption in the Philippines, diffuse irradiance increased an average of 18.8% (10.0 W m −2 ) at three stations and direct irradiance decreased by 7.2% (13.0 W m −2 ).

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“…6 between (H/H0) and (S/S0): (6) Moreover, the average quadratic equation of the model (Eq. 7) was written by Aktağ and Yılmaz [18]:…”

Section: Resultsmentioning

confidence: 99%

“…Many solar energy models have been presented in the literature using mathematical linear [3,4] and nonlinear functions [5,6], artificial neural network [7,8] and fuzzy logic [9].…”

Section: Introductionmentioning

confidence: 99%