Assessment of employing different cloud cover data sources to model the Brazilian solar energy potentiality

Emerson D. Oliveira; Thomás Rocha  Ferreira; Carlos Denyson da Silva Azevedo; Mario de Miranda Vilas Boas Ramos Leitão; Maria Luciene Melo

doi:10.5327/Z2176-94782451

Authors

Emerson D. Oliveira Universidade Federal do Vale do São Francisco (UNIVASF) - Brazil https://orcid.org/0000-0003-1257-2586
Thomás Rocha Ferreira Universidade Federal de Alagoas (UFAL) - Brazil https://orcid.org/0000-0002-9203-0068
Carlos Denyson da Silva Azevedo Instituto Nacional de Pesquisas Espaciais (INPE) - Brazil https://orcid.org/0009-0005-5467-3870
Mario de Miranda Vilas Boas Ramos Leitão Universidade Federal do Vale do São Francisco (UNIVASF) - Brazil https://orcid.org/0000-0003-0179-0115
Maria Luciene Melo Universidade Federal de Alagoas (UFAL) - Brazil https://orcid.org/0000-0001-8388-4240

DOI:

https://doi.org/10.5327/Z2176-94782451

Keywords:

cloud cover; solar radiation; reanalysis data; satellite data; CMIP6 models.

Abstract

A simplified atmospheric transmittance model based on the Beer-Lambert law was utilized to analyze surface solar radiation (SSR) variability based on different sources of cloud cover datasets (CMIP6, ERA5, NCEP, ISCCP, and EUMETSAT). This study evaluated the performance of various modeled SSR datasets against observed data from the Brazilian Daily Weather Gridded Data (BR-DWGD) over the period from 1983 to 2009. Contour plots of annual average SSR from the five modeled datasets were compared with BR-DWGD observations, revealing spatial agreements and discrepancies. The highest SSR values were consistently observed in the Brazilian semi-arid Northeast, while the Amazon region exhibited the lowest values. In the analysis of annual averages, the International Satellite Cloud Climatology Project (ISCCP) demonstrated the closest agreement with BR-DWGD, while the National Center for Environmental Prediction (NCEP) showed the most significant deviations. Root mean square error (RMSE) analysis highlighted seasonal variability in model performance, with the Coupled Model Intercomparison Project Phase 6 (CMIP6) and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) performing best during equinoxes, and ISCCP showing the lowest annual RMSE (16.9 Wm⁻²). Hierarchical clustering further grouped EUMETSAT and CMIP6 as the most similar and accurate datasets, while NCEP remained the least consistent. Global horizontal irradiance maps corroborated SSR patterns, with higher values in the Northeast and lower values in the Amazon and Southern regions. These findings underscored the importance of dataset selection for accurate SSR modeling in Brazil, with ISCCP, EUMETSAT, and CMIP6 emerging as the most reliable options.

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