Quality Control of Global Horizontal Irradiance Estimates through BSRN, TOACs and Air Temperature/Sunshine Duration Test Procedures

Ameen, Bikhtiyar; Balzter, Heiko; Jarvis, C.

doi:10.3390/cli6030069

Cited by 8 publications

(11 citation statements)

References 30 publications

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“…The high rates of positive bias and RMSE at Kalar compared to all of the other stations (Figures 7-11) might be due to the quality of the recorded data [46] and a partial shadow on the sensor at that station, because its mean is lower than that at the other stations by nearly 30 W/m 2 (Table 3), while those data have passed the quality check. However, a similar positive bias and RMSE are reported by other studies [41,42].…”

Section: All-sky Conditionsmentioning

confidence: 97%

“…The two datasets were harmonised for true local solar time. All of the GHI ground data were tested with the BSRN tests [45] and other quality control tests [46,47]. Full information about the quality control of the ground data in this study can be found in [46].…”

Section: Quality Controal Of Ghi Measurmentsmentioning

confidence: 99%

“…All of the GHI ground data were tested with the BSRN tests [45] and other quality control tests [46,47]. Full information about the quality control of the ground data in this study can be found in [46]. Systematic errors were removed from the data, and some questionable values of data according to various tests were not used in the validation process.…”

Section: Quality Controal Of Ghi Measurmentsmentioning

confidence: 99%

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Validation of Hourly Global Horizontal Irradiance for Two Satellite-Derived Datasets in Northeast Iraq

et al. 2018

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Several sectors need global horizontal irradiance (GHI) data for various purposes. However, the availability of a long-term time series of high quality in situ GHI measurements is limited. Therefore, several studies have tried to estimate GHI by re-analysing climate data or satellite images. Validation is essential for the later use of GHI data in the regions with a scarcity of ground-recorded data. This study contributes to previous studies that have been carried out in the past to validate HelioClim-3 version 5 (HC3v5) and the Copernicus Atmosphere Monitoring Service, using radiation service version 3 (CRSv3) data of hourly GHI from satellite-derived datasets (SDD) with nine ground stations in northeast Iraq, which have not been used previously. The validation is carried out with station data at the pixel locations and two other data points in the vicinity of each station, which is something that is rarely seen in the literature. The temporal and spatial trends of the ground data are well captured by the two SDDs. Correlation ranges from 0.94 to 0.97 in all-sky and clear-sky conditions in most cases, while for cloudy-sky conditions, it is between 0.51–0.72 and 0.82–0.89 for the clearness index. The bias is negative for most of the cases, except for three positive cases. It ranges from −7% to 4%, and −8% to 3% for the all-sky and clear-sky conditions, respectively. For cloudy-sky conditions, the bias is positive, and differs from one station to another, from 16% to 85%. The root mean square error (RMSE) ranges between 12–20% and 8–12% for all-sky and clear-sky conditions, respectively. In contrast, the RMSE range is significantly higher in cloudy-sky conditions: above 56%. The bias and RMSE for the clearness index are nearly the same as those for the GHI for all-sky conditions. The spatial variability of hourly GHI SDD differs only by 2%, depending on the station location compared to the data points around each station. The variability of two SDDs is quite similar to the ground data, based on the mean and standard deviation of hourly GHI in a month. Having station data at different timescales and the small number of stations with GHI records in the region are the main limitations of this analysis.

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Section: All-sky Conditionsmentioning

confidence: 97%

Section: Quality Controal Of Ghi Measurmentsmentioning

confidence: 99%

Section: Quality Controal Of Ghi Measurmentsmentioning

confidence: 99%

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Validation of Hourly Global Horizontal Irradiance for Two Satellite-Derived Datasets in Northeast Iraq

et al. 2018

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“…A full quality control procedure was applied to all nine stations of ground measurements to detect systematic errors, to select questionable data and to find gaps and not applicable (NA) values. The quality control procedure is published in a study with full details [73].…”

Section: Quality Control Of Ghi Measurements and Evolution Of Sddsmentioning

confidence: 99%

“…The output data from the nine stations in the study area in the two mentioned studies from which ground measurements were tested [73] and SDDs were evaluated [30], were used in this study.…”

Section: Quality Control Of Ghi Measurements and Evolution Of Sddsmentioning

confidence: 99%

Modelling Hourly Global Horizontal Irradiance from Satellite-Derived Datasets and Climate Variables as New Inputs with Artificial Neural Networks

et al. 2019

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More accurate data of hourly Global Horizontal Irradiance (GHI) are required in the field of solar energy in areas with limited ground measurements. The aim of the research was to obtain more precise and accurate hourly GHI by using new input from Satellite-Derived Datasets (SDDs) with new input combinations of clear sky (Cs) and top-of-atmosphere (TOA) irradiance on the horizontal surface and with observed climate variables, namely Sunshine Duration (SD), Air Temperature (AT), Relative Humidity (RH) and Wind Speed (WS). The variables were placed in ten different sets as models in an artificial neural network with the Levenberg–Marquardt training algorithm to obtain results from training, validation and test data. It was applied at two station types in northeast Iraq. The test data results with observed input variables (correlation coefficient (r) = 0.755, Root Mean Square Error (RMSE) = 33.7% and bias = 0.3%) are improved with new input combinations for all variables (r = 0.983, RMSE = 9.5% and bias = 0.0%) at four automatic stations. Similarly, they improved at five tower stations with no recorded SD (from: r = 0.601, RMSE = 41% and bias = 0.7% to: r = 0.976, RMSE = 11.2% and bias = 0.0%). The estimation of hourly GHI is slightly enhanced by using the new inputs.

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Estimating hourly land surface downward shortwave and photosynthetically active radiation from DSCOVR/EPIC observations

Hao

Asrar

Zeng

et al. 2019

Remote Sensing of Environment

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Quality Control of Global Horizontal Irradiance Estimates through BSRN, TOACs and Air Temperature/Sunshine Duration Test Procedures

Cited by 8 publications

References 30 publications

Validation of Hourly Global Horizontal Irradiance for Two Satellite-Derived Datasets in Northeast Iraq

Validation of Hourly Global Horizontal Irradiance for Two Satellite-Derived Datasets in Northeast Iraq

Modelling Hourly Global Horizontal Irradiance from Satellite-Derived Datasets and Climate Variables as New Inputs with Artificial Neural Networks

Estimating hourly land surface downward shortwave and photosynthetically active radiation from DSCOVR/EPIC observations

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