Scientific workflow applied to cartographic modeling to spatializing global solar irradiation in the state of Rio de Janeiro through the Hargreaves-Samani method
Main Author: | |
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Publication Date: | 2022 |
Other Authors: | , |
Format: | Article |
Language: | por |
Source: | Revista Brasileira de Climatologia (Online) |
Download full: | https://ojs.ufgd.edu.br/rbclima/article/view/15467 |
Summary: | The availability of consistent global solar irradiance (Hg) data is restricted due to the low spatial density of stations that perform Hg measurements and/or degradation of meteorological observation networks. An alternative to this problem is the estimation of Hg, through empirical methods based on other meteorological elements obtained more frequently. Among these methods, the Hargreaves-Samani (1985) (HS) stands out for its simplicity and satisfactory performance in different climate conditions. This study aimed to evaluate the HS method for Hg spatialization in the State of Rio de Janeiro (Estado do Rio de Janeiro - ERJ), using data from air temperature extremes and monthly Hg from 17 automatic weather stations (EMA) of the Instituto Nacional de Meteorologia (INMET). For this, a workflow was developed based on regression models and cartographic modeling to spatialize Hg using the Hargreaves-Samani method. Spatialized Hg values were compared with data observed in the EMAs used, based on the coefficient of determination (r²), Willmott's concordance index (d), confidence index (c), and the root means square error (RQME). It was observed that the application of the method presented estimates with high precision (r² > 0.61) and accuracy (d > 0.78 and RQME > 1.02 MJ m-2 d-1) when seasonality was analyzed, but in its spatial analysis, the method showed lower precision (r² > 0.03) and accuracy (d > 0.53 and RQME > 0.55 MJ m-2 d-1). The proposed workflow presents satisfactory performance to represent the monthly Hg pattern in the ERJ. |
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Scientific workflow applied to cartographic modeling to spatializing global solar irradiation in the state of Rio de Janeiro through the Hargreaves-Samani methodWorkflow científico aplicado al modelado cartográfico para espacializar la irradiación solar global en el estado de Rio de Janeiro por el método de Hargreaves-SamaniWorkflow científico aplicado à modelagem cartográfica para espacialização da irradiação solar global no estado do Rio de Janeiro pelo método de Hargreaves-SamaniRadiação solarTemperatura do ArModelos de RegressãoTransmissividade AtmosféricaThe availability of consistent global solar irradiance (Hg) data is restricted due to the low spatial density of stations that perform Hg measurements and/or degradation of meteorological observation networks. An alternative to this problem is the estimation of Hg, through empirical methods based on other meteorological elements obtained more frequently. Among these methods, the Hargreaves-Samani (1985) (HS) stands out for its simplicity and satisfactory performance in different climate conditions. This study aimed to evaluate the HS method for Hg spatialization in the State of Rio de Janeiro (Estado do Rio de Janeiro - ERJ), using data from air temperature extremes and monthly Hg from 17 automatic weather stations (EMA) of the Instituto Nacional de Meteorologia (INMET). For this, a workflow was developed based on regression models and cartographic modeling to spatialize Hg using the Hargreaves-Samani method. Spatialized Hg values were compared with data observed in the EMAs used, based on the coefficient of determination (r²), Willmott's concordance index (d), confidence index (c), and the root means square error (RQME). It was observed that the application of the method presented estimates with high precision (r² > 0.61) and accuracy (d > 0.78 and RQME > 1.02 MJ m-2 d-1) when seasonality was analyzed, but in its spatial analysis, the method showed lower precision (r² > 0.03) and accuracy (d > 0.53 and RQME > 0.55 MJ m-2 d-1). The proposed workflow presents satisfactory performance to represent the monthly Hg pattern in the ERJ.La disponibilidad de datos consistentes de irradiancia solar (Hg) global está restringida debido a la baja densidad espacial de las estaciones que realizan mediciones de Hg y/o la degradación de las redes de observación meteorológica. Una alternativa a este problema es la estimación de Hg, mediante métodos empíricos basados en otros elementos meteorológicos obtenidos con mayor frecuencia. Entre estos métodos, el de Hargreaves-Samani (1985) (HS) destaca por su sencillez y desempeño satisfactorio en diferentes condiciones climáticas. Este estudio tuvo como objetivo evaluar el método HS para la espacialización de Hg en el Estado de Río de Janeiro (ERJ), utilizando datos de temperaturas extremas del aire y Hg mensuales de 17 estaciones meteorológicas automáticas (EMA) del Instituto Nacional de Meteorología (INMET). Para ello se desarrolló un flujo de trabajo basado en modelos de regresión y modelado cartográfico para espacializar Hg utilizando el método de Hargreaves-Samani. Los valores de Hg espacializados se compararon con los datos observados en los EMA utilizados, con base en el coeficiente de determinación (r²), el índice de concordancia de Willmott (d), el índice de confianza (c) y la raíz del error cuadrático medio (RQME). Se observó que la aplicación del método presentó estimaciones con alta precisión (r² > 0.61) y exactitud (d > 0.78 y RQME > 1.02 MJ m-2 d-1) cuando se analizó la estacionalidad, pero en su análisis espacial, el método mostró menor precisión (r² > 0,03) y exactitud (d > 0,53 y RQME > 0,55 MJ m-2 d-1). El flujo de trabajo propuesto presenta un rendimiento satisfactorio para representar el patrón mensual de Hg en la ERJ.A disponibilidade de dados de irradiação solar global (Hg) consistentes é restrita devido à baixa densidade espacial das estações que realizam medições de Hg e, ou a degradação das redes de observações meteorológicas. Uma alternativa para este problema é a estimativa de Hg, por meio de métodos empíricos baseados em outros elementos meteorológicos obtidos com maior frequência. Dentre esses métodos, destaca-se o de Hargreaves-Samani (1985) (HS) pela simplicidade e desempenho satisfatório em diversas condições climáticas. Este trabalho teve como objetivo avaliar o método de HS para espacialização de Hg no Estado do Rio de Janeiro (ERJ), utilizando-se de dados dos extremos de temperaturas do ar e Hg mensal de 17 estações meteorológicas automáticas (EMA) do Instituto Nacional de Meteorologia (INMET). Para isto, elaborou-se um workflow baseado em modelos de regressão e modelagem cartográfica para espacializar Hg pelo método de Hargreaves-Samani. Os valores de Hg espacializados foram comparados com dados observados nas EMA utilizadas, com base no coeficiente de determinação (r²), índice de concordância de Willmott (d), índice de confiança (c) e a raiz do quadrado médio do erro (RQME). Observou-se que a aplicação do método apresentou estimativas com alta precisão (r² > 0,61) e exatidão (d > 0,78 e RQME > 1,02 MJ m-2 d-1) quando analisada a sazonalidade, porém em sua análise espacial o método apresentou precisão (r² > 0,03) e exatidão inferiores (d > 0,53 e RQME > 0,55 MJ m-2 d-1). O workflow proposto apresenta desempenho satisfatório para representar o padrão mensal de Hg no ERJ.Universidade Federal da Grande Dourados2022-05-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtigo avaliado pelos Paresapplication/pdfhttps://ojs.ufgd.edu.br/rbclima/article/view/1546710.55761/abclima.v30i18.15467Brazilian Journal of Climatology; Vol. 30 (2022); 626 - 646Revista Brasileña de Climatología; Vol. 30 (2022); 626 - 646Journal Brésilien de Climatologie ; Vol. 30 (2022); 626 - 646Revista Brasileira de Climatologia; v. 30 (2022); 626 - 6462237-86422237-864210.55761/abclima.v30i18reponame:Revista Brasileira de Climatologia (Online)instname:ABClimainstacron:ABCLIMAporhttps://ojs.ufgd.edu.br/rbclima/article/view/15467/8594Copyright (c) 2022 Marciano da Costa Lima, Gustavo Bastos Lyra, Anderson Amorim Rocha Santosinfo:eu-repo/semantics/openAccessLima, Marciano da CostaLyra, Gustavo BastosSantos, Anderson Amorim Rocha2022-07-28T21:52:09Zoai:ojs.pkp.sfu.ca:article/15467Revistahttps://revistas.ufpr.br/revistaabclima/indexPUBhttps://revistas.ufpr.br/revistaabclima/oaiegalvani@usp.br || rbclima2014@gmail.com2237-86421980-055Xopendoar:2022-07-28T21:52:09Revista Brasileira de Climatologia (Online) - ABClimafalse |
dc.title.none.fl_str_mv |
Scientific workflow applied to cartographic modeling to spatializing global solar irradiation in the state of Rio de Janeiro through the Hargreaves-Samani method Workflow científico aplicado al modelado cartográfico para espacializar la irradiación solar global en el estado de Rio de Janeiro por el método de Hargreaves-Samani Workflow científico aplicado à modelagem cartográfica para espacialização da irradiação solar global no estado do Rio de Janeiro pelo método de Hargreaves-Samani |
title |
Scientific workflow applied to cartographic modeling to spatializing global solar irradiation in the state of Rio de Janeiro through the Hargreaves-Samani method |
spellingShingle |
Scientific workflow applied to cartographic modeling to spatializing global solar irradiation in the state of Rio de Janeiro through the Hargreaves-Samani method Lima, Marciano da Costa Radiação solar Temperatura do Ar Modelos de Regressão Transmissividade Atmosférica |
title_short |
Scientific workflow applied to cartographic modeling to spatializing global solar irradiation in the state of Rio de Janeiro through the Hargreaves-Samani method |
title_full |
Scientific workflow applied to cartographic modeling to spatializing global solar irradiation in the state of Rio de Janeiro through the Hargreaves-Samani method |
title_fullStr |
Scientific workflow applied to cartographic modeling to spatializing global solar irradiation in the state of Rio de Janeiro through the Hargreaves-Samani method |
title_full_unstemmed |
Scientific workflow applied to cartographic modeling to spatializing global solar irradiation in the state of Rio de Janeiro through the Hargreaves-Samani method |
title_sort |
Scientific workflow applied to cartographic modeling to spatializing global solar irradiation in the state of Rio de Janeiro through the Hargreaves-Samani method |
author |
Lima, Marciano da Costa |
author_facet |
Lima, Marciano da Costa Lyra, Gustavo Bastos Santos, Anderson Amorim Rocha |
author_role |
author |
author2 |
Lyra, Gustavo Bastos Santos, Anderson Amorim Rocha |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Lima, Marciano da Costa Lyra, Gustavo Bastos Santos, Anderson Amorim Rocha |
dc.subject.por.fl_str_mv |
Radiação solar Temperatura do Ar Modelos de Regressão Transmissividade Atmosférica |
topic |
Radiação solar Temperatura do Ar Modelos de Regressão Transmissividade Atmosférica |
description |
The availability of consistent global solar irradiance (Hg) data is restricted due to the low spatial density of stations that perform Hg measurements and/or degradation of meteorological observation networks. An alternative to this problem is the estimation of Hg, through empirical methods based on other meteorological elements obtained more frequently. Among these methods, the Hargreaves-Samani (1985) (HS) stands out for its simplicity and satisfactory performance in different climate conditions. This study aimed to evaluate the HS method for Hg spatialization in the State of Rio de Janeiro (Estado do Rio de Janeiro - ERJ), using data from air temperature extremes and monthly Hg from 17 automatic weather stations (EMA) of the Instituto Nacional de Meteorologia (INMET). For this, a workflow was developed based on regression models and cartographic modeling to spatialize Hg using the Hargreaves-Samani method. Spatialized Hg values were compared with data observed in the EMAs used, based on the coefficient of determination (r²), Willmott's concordance index (d), confidence index (c), and the root means square error (RQME). It was observed that the application of the method presented estimates with high precision (r² > 0.61) and accuracy (d > 0.78 and RQME > 1.02 MJ m-2 d-1) when seasonality was analyzed, but in its spatial analysis, the method showed lower precision (r² > 0.03) and accuracy (d > 0.53 and RQME > 0.55 MJ m-2 d-1). The proposed workflow presents satisfactory performance to represent the monthly Hg pattern in the ERJ. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-05-05 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Artigo avaliado pelos Pares |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://ojs.ufgd.edu.br/rbclima/article/view/15467 10.55761/abclima.v30i18.15467 |
url |
https://ojs.ufgd.edu.br/rbclima/article/view/15467 |
identifier_str_mv |
10.55761/abclima.v30i18.15467 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://ojs.ufgd.edu.br/rbclima/article/view/15467/8594 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2022 Marciano da Costa Lima, Gustavo Bastos Lyra, Anderson Amorim Rocha Santos info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2022 Marciano da Costa Lima, Gustavo Bastos Lyra, Anderson Amorim Rocha Santos |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal da Grande Dourados |
publisher.none.fl_str_mv |
Universidade Federal da Grande Dourados |
dc.source.none.fl_str_mv |
Brazilian Journal of Climatology; Vol. 30 (2022); 626 - 646 Revista Brasileña de Climatología; Vol. 30 (2022); 626 - 646 Journal Brésilien de Climatologie ; Vol. 30 (2022); 626 - 646 Revista Brasileira de Climatologia; v. 30 (2022); 626 - 646 2237-8642 2237-8642 10.55761/abclima.v30i18 reponame:Revista Brasileira de Climatologia (Online) instname:ABClima instacron:ABCLIMA |
instname_str |
ABClima |
instacron_str |
ABCLIMA |
institution |
ABCLIMA |
reponame_str |
Revista Brasileira de Climatologia (Online) |
collection |
Revista Brasileira de Climatologia (Online) |
repository.name.fl_str_mv |
Revista Brasileira de Climatologia (Online) - ABClima |
repository.mail.fl_str_mv |
egalvani@usp.br || rbclima2014@gmail.com |
_version_ |
1832009312406339584 |