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

Abstract

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.

Convection‐Permitting ICON‐LAM Simulations for Renewable Energy Potential Estimates Over Southern Africa

“…(2023a) reported that the MAE varies from 153 to 232 W m −2 for the rsds under cloudy skies for the reanalysis (ERA5 and MERRA‐2) and satellite (CAMS and SARAH‐2) products, compared to 37 observing stations over western Africa. Evaluation of satellite‐estimated rsds over South Africa (Mabasa et al., 2022) showed excellent performance under clear skies with rMAE smaller than 6.5% and poorer performance under cloudy skies with rMAE 29%, whereas satellite‐based rsds estimate outperform the reanalysis‐based estimate over South Africa (Mabasa et al., 2021). In addition, a dynamical downscaling study (Sawadogo et al., 2023b) using the WRF‐solar model driven by ERA5 also documented large overestimations for rsds under cloudy skies.…”

“…Additionally, both data sources have a larger error of more than 150 W m 2 (root mean squared error) under cloudy skies than under clear skies. Mabasa et al (2021) conducted an evaluation of GHI using two reanalyzes (ERA5 and MERRA2) and three satellite-based data sets (SOLCAST, CAMS, and SARAH-2) against 13 radiometric observations in South Africa. The study found that both types of data sets overestimate GHI, with less discrepancy observed in satellite-based GHI compared to reanalysis-based GHI.…”

“…It has a grid spacing of 0.25°with a temporal resolution of 3 hr and a continuous temporal coverage.Satellite driven rsds observations have been widely used to evaluate climate model simulations(Bichet et al, 2019;Sawadogo et al, 2021; Tang et al, 2019). Surface Solar Radiation Parameters edition 3 (SARAH-3)(Pfeifroth et al, 2023) is the successor of the SARAH-2(Pfeifroth et al, 2017), which has been proved to provide viable solar energy assessment data over Africa(Mabasa et al, 2021;Sawadogo et al, 2023a); SARAH-3 is provided by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Climate Monitoring (CM SAF). It provides rsds based on geostationary satellite observations with a half hourly temporal resolution and a high spatial resolution of 0.05°, covering an area from 65°E to 65°E and 65°N to 65°N.…”

Calibration and Validation of Global Horizontal Irradiance Clear Sky Models against McClear Clear Sky Model in Morocco

“…The models considered ranged from simple clear sky models (Berger-Duffie, ABCG, and Haurwitz) that require few input parameters (such as the sun's zenith angle (θ z ) and the top of the atmosphere's direct normal irradiance (DN I TOA )) to complex clear sky models (Ineichen and Perez, Bird, Simplified Solis) that require many input parameters in addition to θ z and DN I TOA , as summarized in Table 3. The SOLCAST satellite-based dataset was also included in the study because it performed best when validated against GHI observation data such as Mabasa et al [39] in South Africa, Yang and Bright [40] globally using Baseline Surface Radiation Network (BSRN) [41] stations, and Bright [42] in all climatic regions. It was included in the study to investigate the possibility of using it with ERA5 hourly fractional cloud cover data [43] in estimating clear sky irradiance.…”

Comparison of Ground-Based Global Horizontal Irradiance and Direct Normal Irradiance with Satellite-Based SUNY Model

“…A lot of studies were conducted on ground-based data and satellite-based data [24][25][26][27][28][29]. Some of studies done in the past showed that there were various errors associated with satellite-based models and that is why there was a difference between solar data obtained from satellite models and ground measurements [30][31][32].…”