Fast radiative transfer parameterisation for assessing the surface solar irradiance: The Heliosat‑4 method

Qu, Zhipeng; Oumbe, Armel; Blanc, Philippe; Espinar, Bella; Gesell, Gerhard; Gschwind, Benoît; Klüser, Lars; Lefèvre, Mireille; Saboret, Laurent; Schroedter-Homscheidt, Marion; Wald, Lucien

doi:10.1127/metz/2016/0781

Cited by 179 publications

(108 citation statements)

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“…A possible explanation of this is that the GSIP model reports lower G s under clear-sky conditions, especially around solar noon (low solar zenith angle), when the solar irradiance values are the highest [17]. The correlation coefficients are the smallest at the largest solar zenith angle interval, which could be a result of less accuracy in the retrieval of cloud properties from satellites ( Figure 8) [40].…”

Section: Discussionmentioning

confidence: 84%

“…Also, for both sites the uncertainties in the estimation of cloud properties (cover, type and layer) could also be another source of error. The rapid changes in cloud cover can be inferred from the ground measurements, especially at the finer resolution at the ASF site [40,41]. The different areas that the satellite sensor and ground pyranometer represent could also cause some error, especially for partially cloud-covered conditions.…”

Section: Discussionmentioning

confidence: 99%

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An Evaluation of Satellite Estimates of Solar Surface Irradiance Using Ground Observations in San Antonio, Texas, USA

et al. 2017

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Abstract:Estimates of solar irradiance at the earth's surface from satellite observations are useful for planning both the deployment of distributed photovoltaic systems and their integration into electricity grids. In order to use surface solar irradiance from satellites for these purposes, validation of its accuracy against ground observations is needed. In this study, satellite estimates of surface solar irradiance from Geostationary Operational Environmental Satellite (GOES) are compared with ground observations at two sites, namely the main campus of the University of Texas at San Antonio (UTSA) and the Alamo Solar Farm of San Antonio (ASF). The comparisons are done mostly on an hourly timescale, under different cloud conditions classified by cloud types and cloud layers, and at different solar zenith angle intervals. It is found that satellite estimates and ground observations of surface solar irradiance are significantly correlated (p < 0.05) under all sky conditions (r: 0.80 and 0.87 on an hourly timescale and 0.94 and 0.91 on a daily timescale, respectively for the UTSA and ASF sites); on the hourly timescale, the correlations are 0.77 and 0.86 under clear-sky conditions, and 0.74 and 0.84 under cloudy conditions, respectively for the UTSA and ASF sites, and mostly >0.60 under different cloud types and layers for both sites. The correlations under cloudy-sky conditions are mostly stronger than those under clear-sky conditions at different solar zenith angles. The correlation coefficients are mostly the smallest with solar zenith angle in the range of 75-90 • under all sky, clear-sky and cloudy-sky conditions. At the ASF site, the overall bias of GOES surface solar irradiance is small (+1.77 Wm −2 ) under all sky while relatively larger under clear-sky (−22.29 Wm −2 ) and cloudy-sky (+40.31 Wm −2 ) conditions. The overall good agreement of the satellite estimates with the ground observations underscores the usefulness of the GOES surface solar irradiance estimates for solar energy studies in the San Antonio area.

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Section: Discussionmentioning

confidence: 84%

Section: Discussionmentioning

confidence: 99%

An Evaluation of Satellite Estimates of Solar Surface Irradiance Using Ground Observations in San Antonio, Texas, USA

et al. 2017

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“…The new Heliosat-4 method differs from the previous Heliosat methods (Qu et al, 2016) in its concept. It adopts the approximation of Oumbe et al (2014) where the SSI is approximated by the product of the McClear SSI by a modification factor which depends on solar zenith angle, cloud properties and ground albedo.…”

Section: The Family Of Heliosat Methods the Helioclim Databases And mentioning

confidence: 99%

Evaluating meso-scale change in performance of several databases of hourly surface irradiation in South-eastern Arabic Pensinsula

et al. 2017

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Abstract. The solar hourly global irradiation received at ground level estimated by the databases HelioClim3v4, HelioClim-3v5 and CAMS Radiation Service are compared to coincident measurements made in five stations in Oman and Abu Dhabi. CAMS is an abbreviation of Copernicus Atmosphere Monitoring Service. Each database describes the hour-to-hour changes in irradiation very well with correlation coefficients greater than 0.97 for all stations. Each database exhibits a tendency to underestimate the irradiation in this area; the bias is small and less than 5 % of the average of the measurements in absolute value. The RMSE ranges between 70 and 90 Wh m −2 (11 to 16 %). This validation of the three databases for this arid region on the edge of the Meteosat coverage reveals satisfactory results. Each database captures accurately the temporal and spatial variability of the irradiance field. It is found that the three databases do not exhibit noticeable geographical changes in performances and are reliable sources to assess the SSI in this region.

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“…The CAMS radiation service v2 data set, abbreviated in CRS, is generated by processing images of the Meteosat second generation by the Heliosat 4 method (Qu et al, 2017). The geographical and temporal coverages as well as the spatial and temporal resolutions are the same than HC3v5.…”

Section: The Cams Radiation Service V2 Data Set (Crs)mentioning

confidence: 99%

“…The CAMS Radiation Service provides a classification of the clouds in four types as a function of the altitude (Qu et al, 2017):…”

Section: The Cams Cloud Classificationmentioning

confidence: 99%

Estimating downwelling solar irradiance at the surface of the tropical Atlantic Ocean: A comparison of PIRATA measurements against several re-analyses and satellite-derived data sets

Trolliet

Walawender

Bourlès

et al. 2017

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Abstract. 15This paper assesses the merits and drawbacks of several data sets of the solar downwelling radiation received at the surface of the tropical Atlantic Ocean where the field of solar radiation is hardly known. The data sets are compared to qualified measurements of hourly irradiance made at five buoys of the PIRATA network for the period 2012-2013. The data sets comprise the re-analyses MERRA-2 and ERA-5 and three satellite-derived data sets: HelioClim 3v5, SARAH 2 and CAMS Radiation Service v2. The re-analyses often report cloud-free conditions while actual conditions are cloudy and reciprocally, 20 actual cloudless conditions as cloudy. The medium and high level clouds exhibit more bias than the low level clouds. The reanalyses poorly correlate with the optical state of the atmosphere derived from the measurements. The actual irradiance field is spatially distorted by re-analyses, especially for MERRA-2. Performances are similar between the three satellite-derived data sets. They correlate well with the optical state of the atmosphere and reproduce well the dynamics of the solar irradiance. The three data sets exhibit overestimation with the lowest biases reached by CAMS Radiation Service v2. The 25 bias of HelioClim 3v5 is fairly similar from one location to the other which means that the actual spatial gradients are well reproduced.Ocean Sci. Discuss., https://doi

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Fast radiative transfer parameterisation for assessing the surface solar irradiance: The Heliosat‑4 method

Cited by 179 publications

References 0 publications

An Evaluation of Satellite Estimates of Solar Surface Irradiance Using Ground Observations in San Antonio, Texas, USA

An Evaluation of Satellite Estimates of Solar Surface Irradiance Using Ground Observations in San Antonio, Texas, USA

Evaluating meso-scale change in performance of several databases of hourly surface irradiation in South-eastern Arabic Pensinsula

Estimating downwelling solar irradiance at the surface of the tropical Atlantic Ocean: A comparison of PIRATA measurements against several re-analyses and satellite-derived data sets

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