Estimation of Global Solar Radiation in Unmonitored Areas of Brazil Using ERA5 Reanalysis and Artificial Neural Networks
Year: 2025
Type: article
Abstract: Estimating global radiation (GR) is crucial for assessing solar energy potential, understanding surface energy balance, and forecasting agricultural production. However, several regions require additional monitoring and sparse sensor networks. The ERA5-ECMWF reanalysis is a viable alternative for estimating meteorological elements in unmonitored areas. This study aimed to train an artificial neural network (ANN) model to estimate GR based on ERA5 data and map its distribution in the study area. We utilized GR data from 32 automatic weather stations of the Brazilian National Institute of Meteorology in Mato Grosso, Brazil, for model training. The model input consisted of ERA5 air temperature, precipitation data, and top-of-atmosphere solar radiation (R0) calculated from the latitude and day of the year. The calibrated model demonstrated high accuracy, with Nash–Sutcliffe and Kling–Gupta efficiency indices exceeding 0.99. This enabled the generation of historical time series and maps of GR spatial distribution in the study area. The results demonstrate that—as input for the ANN—ERA5 data enables precise and accurate estimation of GR distribution, even in locations without meteorological stations.
Source: Atmosphere
Authors: Eduardo Morgan Uliana, Juliana de Abreu Araujo, Márcio Roggia Zanuzo, Alvaro Henrique Guedes Araujo, Marionei Fomaca de Sousa Junior, Uilson Ricardo Venâncio Aires, Herval Alves Ramos Filho
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Award: G116629194
OpenAlex ID: W7106005414