Worldwide performance assessment of 75 global clear-sky irradiance models using Principal Component Analysis

Sun, Xiaoming; Bright, Jamie M.; Gueymard, Christian A.; Acord, Brendan; Wang, Peng; Engerer, Nicholas A.

doi:10.1016/j.rser.2019.04.006

Cited by 122 publications

(63 citation statements)

References 87 publications

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“…In order to test the influence of PW on clear-sky radiation modelling, a top-performing model-REST2v5, as detailed by Gueymard (2008)-is selected based on the thorough study from Sun et al (2018). REST2v5 was shown to perform excellently across all Köppen-Geiger climate classifications and is therefore considered a suitable test bed to explore the influence of PW on irradiance.…”

Section: Figmentioning

confidence: 99%

“…Clear-sky radiation modelling is fundamental to all solar forecasting estimates and historical database production (Engerer et al, 2017). Many different clear-sky radiation models have been validated around the world in various studies (Sun et al, 2018;Ineichen, 2016;Engerer and Mills, 2015;Gueymard, 1993Gueymard, , 2012a. In such studies, the most successful models were those that use a specific representation of PW within their derivation.…”

Section: Introductionmentioning

confidence: 99%

“…Some simpler radiation models consider the PW attenuation only indirectly through the Linke turbidity factor, which combines the PW effect with those of aerosols and air mass. Of the 57 clear-sky models analysed by Sun et al (2018), for instance, 52 models have specific consideration for PW. The 5 models that do not consider PW are at least 30-year old and tend to underperform.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Climatic and Global Validation of Daily MODIS Precipitable Water Data at AERONET Sites for Clear-sky Irradiance Modelling

Bright¹,

Gueymard²,

Killinger³

et al. 2018

Proceedings of EuroSun 2018

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Precipitable water (PW) is an influential variable in regards to clear-sky radiation modelling and solar resource assessment. Thus, the accuracy of solar energy estimates depends on the accuracy of PW measurements. Gridded satellite information is commonly used for solar modelling because of its benefit of a broad geographical coverage, thus a global validation of commonly utilised PW products is imperative. Here, all Level-3 Moderate Resolution Imaging Spectroradiometer (MODIS) daily PW products from the Aqua and Terra satellites (at 1°× 1° spatial resolution) from 01/2000 to 02/2018 are compared and validated against all of NASA's ground-sensing Aerosol Robotic NETwork (AERONET) V3 Level-2 PW daily averages from sites that have at least one year of observations during 2000-2018 (452 sites representing 675,158 observations). Furthermore, sub-categorisation by Köppen-Geiger climate regions enables climate specific validation to ascertain any distinct climatic influence. The results demonstrate significant climatological influences that impact the derived PW products. It is found that the Terra PW is more accurate than the Aqua PW, and that blending these two products yields a higher accuracy of daily PW estimates. The MODIS PW product also suffers from overestimation at larger magnitudes (>3 cm). The absolute errors do not reduce linearly with the PW magnitude, so that relative errors are far worse in areas of low PW, such as the polar climate. The equatorial climate, with the highest PW records, behaves best. Finally, a simple sensitivity test using the REST2 clear-sky radiation model shows that the global PW RMSE (0.511 cm) of the combined MODIS data would result in a 1.5-2.5% under-or overestimate on direct normal irradiance (DNI) depending on latitude relative to using the AERONET mean PW of 1.8971 cm. It is thus concluded that the daily MODIS PW product is not ideal for clear-sky radiation modelling, at least whenever accurate DNI predictions are necessary on a global scale.

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Section: Figmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Climatic and Global Validation of Daily MODIS Precipitable Water Data at AERONET Sites for Clear-sky Irradiance Modelling

Bright¹,

Gueymard²,

Killinger³

et al. 2018

Proceedings of EuroSun 2018

Self Cite

View full text Add to dashboard Cite

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“…For clear sky days, clouds do not appear from sunrise to sunset. We used the clear sky model above-mentioned (ESRA model) to calculate the surface solar radiation under clear sky conditions, where in this model, T L is a function of the aerosol optical (AOD) and the atmospheric water vapor [26,38], we used the Equation (10) to calculate T L . The aerosol optical depth in the formula was selected as 0.1 [39] and the water vapor column value was selected as 0.09 cm in autumn and winter in Dunhuang Gobi [40,41].…”

Section: Results Under Clear Sky Conditionsmentioning

confidence: 99%

Nowcasting of Surface Solar Irradiance Using FengYun-4 Satellite Observations over China

Yang

Gao

et al. 2019

Remote Sensing

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The accurate prediction of surface solar irradiance is of great significance for the generation of photovoltaic power. Surface solar irradiance is affected by many random mutation factors, which means that there are great challenges faced in short-term prediction. In Northwest China, there are abundant solar energy resources and large desert areas, which have broad prospects for the development of photovoltaic (PV) systems. For the desert areas in Northwest China, where meteorological stations are scarce, satellite remote sensing data are extremely precious exploration data. In this paper, we present a model using FY-4A satellite images to forecast (up to 15–180 min ahead) global horizontal solar irradiance (GHI), at a 15 min temporal resolution in desert areas under different sky conditions, and compare it with the persistence model (SP). The spatial resolution of the FY-4A satellite images we used was 1 km × 1 km. Particle image velocimetry (PIV) was used to derive the cloud motion vector (CMV) field from the satellite cloud images. The accuracy of the forecast model was evaluated by the ground observed GHI data. The results showed that the normalized root mean square error (nRMSE) ranged from 18.9% to 21.6% and the normalized mean bias error (nMBE) ranged from 3.2% to 4.9% for time horizons from 15 to 180 min under all sky conditions. Compared with the SP model, the nRMSE value was reduced by about 6%, 8%, and 14% with the time horizons of 60, 120, and 180 min, respectively.

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“…This article deals with two of these satellitederived databases of E: the CAMS Radiation Service, abbreviated as CAMS-Rad and part of the Copernicus Atmosphere Monitoring Service (CAMS), and the HelioClim-3 version 5, abbreviated as HC3v5. Both databases are available online (http://www.soda-pro.com, last access: 29 June 2020) and are used by several hundred academics and engineers each year (Thomas et al, 2016a). Many validations have been performed and are still being performed to supply users with the most complete knowledge of the accuracy of each database.…”

Section: Introductionmentioning

confidence: 99%

Performance of CAMS Radiation Service and HelioClim-3 databases of solar radiation at surface: evaluating the spatial variation in Germany

et al. 2020

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Abstract. The present work deals with the spatial consistency of two well-known databases of solar radiation received at ground level: the CAMS Radiation Service database version 3.2, abbreviated as CAMS-Rad and the HelioClim-3 database version 5, abbreviated as HC3v5. Both databases are derived from satellite images. They are validated against 10 min means of irradiance for the period 2010–2018 recorded in a network of 26 ground stations in Germany operated by the Deutscher Wetterdienst (DWD). For the CAMS-Rad database, the correlation coefficient between ground measurements and estimates ranges between 0.83 and 0.92 for all sky conditions. The bias ranges from −41 and 32 W m−2 (−11 % and 10 % of the mean irradiance). The standard deviation ranges between 89 and 129 W m−2 (25 % and 39 %). For the HC3v5 database, the correlation coefficient ranges between 0.90 and 0.95. The bias and the standard deviation are comprised between −22 and 16 W m−2 (−6 % and 5 %), and between respectively 70 and 104 W m−2 (20 % and 31 %). For the CAMS Rad database, overestimation is observed in the South, and underestimation in the North with a faint tendency of the bias to increase from East to West. For the HC3v5 database, the bias is fairly homogeneous across Germany. For both databases, there is no noticeable spatial trend in the standard deviation.

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Worldwide performance assessment of 75 global clear-sky irradiance models using Principal Component Analysis

Cited by 122 publications

References 87 publications

Climatic and Global Validation of Daily MODIS Precipitable Water Data at AERONET Sites for Clear-sky Irradiance Modelling

Climatic and Global Validation of Daily MODIS Precipitable Water Data at AERONET Sites for Clear-sky Irradiance Modelling

Nowcasting of Surface Solar Irradiance Using FengYun-4 Satellite Observations over China

Performance of CAMS Radiation Service and HelioClim-3 databases of solar radiation at surface: evaluating the spatial variation in Germany

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