Verifying the spatial consistency of the CAMS Radiation Service and HelioClim-3 satellite-derived databases of solar radiation using a dense network of measuring stations: the case of The Netherlands

Marchand, Mathilde; Lefèvre, Mireille; Saboret, Laurent; Wey, Etienne; Wald, Lucien

doi:10.5194/asr-16-103-2019

Cited by 11 publications

(5 citation statements)

References 32 publications

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“…The MBE averaged across sites ranged from 1% to 21% between methods, with a tendency for all methods to generally overestimate PAR (positive biases). One of the reasons for this might have been the overestimation of CAM-Rad and HC3 in overcast situations [26,61]. Aside from M3 and M8, the MBE did not exceed 10%.…”

Section: Resultsmentioning

confidence: 96%

Smart Approaches for Evaluating Photosynthetically Active Radiation at Various Stations Based on MSG Prime Satellite Imagery

et al. 2023

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Photosynthetically active radiation (PAR) is the 400–700 nm portion of the solar radiation spectrum that photoautotrophic organisms including plants, algae, and cyanobacteria use for photosynthesis. PAR is a key variable in global ecosystem and Earth system modeling, playing a prominent role in carbon and water cycling. Alongside air temperature, water availability, and atmospheric CO2 concentration, PAR controls photosynthesis and consequently biomass productivity in general. The management of agricultural and horticultural crops, forests, grasslands, and even grasses at sports venues is a non-exhaustive list of applications for which an accurate knowledge of the PAR resource is desirable. Modern agrivoltaic systems also require a good knowledge of PAR in conjunction with the variables needed to monitor the co-located photovoltaic system. In situ quality-controlled PAR sensors provide high-quality information for specific locations. However, due to associated installation and maintenance costs, such high-quality data are relatively scarce and generally extend over a restricted and sometimes non-continuous period. Numerous studies have already demonstrated the potential offered by surface radiation estimates based on satellite information as reliable alternatives to in situ measurements. The accuracy of these estimations is site-dependent and is related, for example, to the local climate, landscape, and viewing angle of the satellite. To assess the accuracy of PAR satellite models, we inter-compared 11 methods for estimating 30 min surface PAR based on satellite-derived estimations at 33 ground-based station locations over several climate regions in Europe, Africa, and South America. Averaged across stations, the results showed average relative biases (relative to the measurement mean) across methods of 1 to 20%, an average relative standard deviation of 25 to 30%, an average relative root mean square error of 25% to 35% and a correlation coefficient always above 0.95 for all methods. Improved performance was seen for all methods at relatively cloud-free sites, and quality degraded towards the edge of the Meteosat Second Generation viewing area. A good compromise between computational time, memory allocation, and performance was achieved for most locations using the Jacovides coefficient applied to the global horizontal irradiance from HelioClim-3 or the CAMS Radiation Service. In conclusion, satellite estimations can provide a reliable alternative estimation of ground-based PAR for most applications.

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Section: Resultsmentioning

confidence: 96%

Smart Approaches for Evaluating Photosynthetically Active Radiation at Various Stations Based on MSG Prime Satellite Imagery

et al. 2023

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“…optical depth, type and coverage of cloud) derived from MSG observations, every 15 min and at 5 km of resolution, to calculate the cloudy sky irradiance. The Heliosat‐4 model generally exhibits satisfactory performance (Marchand et al ., 2019) although some limitations are known (Qu et al ., 2017). The CAMS products are related to the solar radiation incoming on a horizontal surface of a certain site, for the actual weather conditions, at different time steps (from 1 min to 1 month).…”

Section: Radiation Estimation Methodsmentioning

confidence: 99%

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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“…Merchand et al [27] validated the CAMS satellite-based dataset against hourly GHI from 16 stations from the Royal Meteorological Institute KNMI in the Netherlands, a temperature climate without a dry season and warm winter. The reference stations were located inland and along the coast.…”

Section: Literature Reviewmentioning

confidence: 99%

Validating Hourly Satellite Based and Reanalysis Based Global Horizontal Irradiance Datasets over South Africa

Mabasa

Lysko²,

Moloi

2021

Geomatics

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This study validates the hourly satellite based and reanalysis based global horizontal irradiance (GHI) for sites in South Africa. Hourly GHI satellite based namely: SOLCAST, Copernicus Atmosphere Monitoring Service (CAMS), and Satellite Application Facility on Climate Monitoring (CMSAF SARAH) and two reanalysis based, namely, fifth generation European Center for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5) and Modern-Era Retrospective Analysis for Research and Applications (MERRA2) were assessed by comparing in situ measured data from 13 South African Weather Service radiometric stations, located in the country’s six macro climatological regions, for the period 2013–2019. The in situ data were first quality controlled using the Baseline Surface Radiation Network methodology. Data visualization and statistical metrics relative mean bias error (rMBE), relative root mean square error (rRMSE), relative mean absolute error (rMAE), and the coefficient of determination (R2) were used to evaluate the performance of the datasets. There was very good correlation against in situ GHI for the satellite based GHI, all with R2 above 0.95. The R2 correlations for the reanalysis based GHI were less than 0.95 (0.931 for ERA5 and 0.888 for MERRA2). The satellite and reanalysis based GHI showed a positive rMBE (SOLCAST 0.81%, CAMS 2.14%, CMSAF 2.13%, ERA5 1.7%, and MERRA2 11%), suggesting consistent overestimation over the country. SOLCAST satellite based GHI showed the best rRMSE (14%) and rMAE (9%) combinations. MERRA2 reanalysis based GHI showed the weakest rRMSE (37%) and rMAE (22%) combinations. SOLCAST satellite based GHI showed the best overall performance. When considering only the freely available datasets, CAMS and CMSAF performed better with the same overall rMBE (2%), however, CAMS showed slightly better rRMSE (16%), rMAE (10%), and R2 (0.98) combinations than CMSAF rRMSE (17%), rMAE (11%), and R2 (0.97). CAMS and CMSAF are viable freely available data sources for South African locations.

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Verifying the spatial consistency of the CAMS Radiation Service and HelioClim-3 satellite-derived databases of solar radiation using a dense network of measuring stations: the case of The Netherlands

Cited by 11 publications

References 32 publications

Smart Approaches for Evaluating Photosynthetically Active Radiation at Various Stations Based on MSG Prime Satellite Imagery

Smart Approaches for Evaluating Photosynthetically Active Radiation at Various Stations Based on MSG Prime Satellite Imagery

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

Validating Hourly Satellite Based and Reanalysis Based Global Horizontal Irradiance Datasets over South Africa

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