Global radiation by simplified models for the state of Mato Grosso, Brazil

Souza, Adilson Pacheco de; Silva, Andréa Carvalho da; Tanaka, Adriana Aki; Uliana, Eduardo Morgan; Almeida, Frederico Terra de; Klar, Antônio Evaldo; Gomes, Anthony Wellington Almeida

doi:10.1590/s0100-204x2017000400001

Cited by 8 publications

(30 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…e second set was used to validate performance (step 2) ( Table 1). e data were divided differently for each location based on the number of valid H s daily data from the database, of which 70% were used for calibration and 30% for validation [1,13].…”

Section: Observed Climate Data Mg Is Located In Seb (mentioning

confidence: 99%

“…Global solar radiation (H s ) directly influences the physical, chemical, and biological processes that occur in the biosphere atmosphere [1]. Spatial, temporal, and seasonal variations in H s influence planning and decision-making activities related to many fields, such as the energy, hydrological, environmental, agricultural, and meteorological areas [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

“…Although H s is an important variable, recorded data have been restricted to automatic weather stations (AWSs) [1,3,8], and the number of AWSs capable of registering these data has fallen due to the high costs associated with acquiring, maintaining, and calibrating the instruments [9][10][11][12]. Furthermore, when H s is available in the AWS, it does not present a long time series [13].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, when H s is available in the AWS, it does not present a long time series [13]. ere is a general lack of data coming from most Brazilian AWSs [1,13] even in the most developed regions of the country [14]. For example, according to the Integrated Environmental Data System (Sistema Integrado de Dados Ambientais (SINDA)), of the 61 AWSs in operation in the Minas Gerais (MG) state (Figure 1), only 32 have H s measurement capabilities, and of these 32, only 10 have H s records available (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

“…ere are many existing EMs, which differ in both their complexity and the input variables considered [1,3,5,16].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Global Solar Radiation Models in Minas Gerais, Southeastern Brazil

Monteiro

Martins

2019

Advances in Meteorology

View full text Add to dashboard Cite

Accurate and complete global solar radiation (Hs) data at a specific region are crucial for regional climate assessment, crop growth modeling, and all operations that use solar energy. However, in the Minas Gerais state, Southeastern Brazil (SEB), the number of weather stations that measure global solar radiation is scarce, and when it is available, it presents gaps in the time series. An attractive alternative to solve the data gap problem is to estimate global solar radiation using empirical models. In this study, thirteen models based on maximum and minimum air temperatures, precipitation, sunshine duration, and extraterrestrial solar radiation were compared in the daily estimation of Hs. Data from 10 weather stations, from 1999 to 2017, located in Minas Gerais were used. Also, cluster analysis was used to group the localities (weather stations) with similar patterns of model performance, climatic classification (Köppen–Geiger and Thornthwaite), and seasonal data variability, considering minimum and maximum air temperatures, precipitation, sunshine duration, and global solar radiation. Although it is apparently simple, studies on this subject are scarce and the few existing ones in Minas Gerais have flaws, which justifies this study. The models were evaluated by root mean square error (RMSE), mean absolute percentage error (MAPE), mean bias error (BIAS), Willmott’s index of agreement (d), and performance index (c-index). Models based on sunshine duration, such as those proposed by Ertekin and Yaldiz and by Newland, showed the best performance (average c-index = 0.71). Models based on temperature and precipitation showed the worst results (average c-index = 0.41). Cluster analysis showed that there is a similar pattern between the performance of the models, climatic classification, and seasonal variability of data among the localities of Minas Gerais. In general, models that presented extremely poor performance were formed with weather stations located in the dry zone, but with different climate classification, and models that presented very good (and good) performance were composed by weather stations located in the humid zone (dry subhumid) with the same climate classification and similar seasonal variability. Furthermore, the models based on temperature have a tendency to overestimate radiation values below 10 MJ·m−2 day−1 and to underestimate values higher than 25 MJ·m−2 day−1. This point is a limitation of the model for estimating global solar radiation below and above these levels, showing the influence of atmospheric systems and atmospheric attenuation mechanisms of global solar radiation.

show abstract

Section: Observed Climate Data Mg Is Located In Seb (mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…ere are many existing EMs, which differ in both their complexity and the input variables considered [1,3,5,16].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Global Solar Radiation Models in Minas Gerais, Southeastern Brazil

Monteiro

Martins

2019

Advances in Meteorology

View full text Add to dashboard Cite

show abstract

Imputação De Dados Diários De Radiação Solar Global via Ambiente R

Silva¹,

Jiménez²,

Pereira³

et al. 2019

Enci Bio

View full text Add to dashboard Cite

O objetivo do estudo foi avaliar a técnica de imputação de dados via software Ambiente R na série temporal diária de radiação solar global (Q g) no município de Arapiraca, Alagoas-Nordeste Brasileiro (NEB). A série temporal de radiação solar global de 2008 a 2016 foi obtida do INMET. A partir dos percentuais de falhas na faixa entre 0,05% e 0,15%, foi aplicado o método de imputação múltipla aos dados de Q g , via pacote MICE. O teste de PBIAS apontou superestimava da radiação solar global observada (R 0) em Arapiraca (-7,40). Valores altos de NSE (0,90) e R 2 (0,99) também foram identificados na estação Arapiraca. Isso mostrou um desempenho muito bom do pacote MICE entre R 0 e a radiação solar global imputada (R i). Os diagramas de dispersão da R 0 e R i mostrou correlação (r) quase perfeita e os índices de concordância (d) e confiança (c) categorizados como ótimo e muito bom, em Arapiraca (r = 0,99 e d = 0,97). Os parâmetros EPE, RMSE e MAE indicaram uma baixa dispersão do modelo de regressão linear simples em Arapiraca EPE (113 KJ.m-2), RMSE (137,49 KJ.m-2) e MAE (111,47 KJ.m-2). O teste t de Student aplicado à série temporal de Q g mostrou que a H 0 foi rejeitada para p. valor < 0,05.

show abstract

Rainfall Erosivity in Municipalities of the Brazilian Cerrado Biome

Castagna

Souza

Vendrusculo

et al. 2022

NAT

View full text Add to dashboard Cite

The objective of this work was to estimate the rainfall erosivity in 101 municipalities in the Brazilian Cerrado biome. First, a filling of missing data was carried out for 81 rain gauge stations, with a 20-year historical series, in the states of Mato Grosso (MT), Mato Grosso do Sul (MS), Minas Gerais (MG), and Goiás (GO). The corrected data were subjected to 16 regionally calibrated equations, which enabled the determination of the rainfall erosivity. The results indicated that municipalities in northeastern MS present the highest monthly erosivity indexes (El30m), reaching 2,796.5 MJ mm ha-1 h-1 year-1 in January, whereas the lowest index for this same month was 733.0 MJ mm ha-1 h-1 in the eastern MT. The rainfall annual erosivity, or R factor, varied between 3,713.1 and 12,345.6 MJ mm ha-1 h-1 year-1, with the lowest values for municipalities in eastern MT and the highest values for those in northeastern MS. Although the municipalities studied are within the same biome, the spatial distribution makes them present regional effects due to different climatic factors, resulting in different rainfall volumes and intensities, which is reflected in the results of the monthly erosivity index and R factor. Keywords: rainfall stations; R fator; fill missing data; precipitation. Erosividade da chuva em municípios do Cerrado brasileiro RESUMO: O objetivo deste trabalho é estimar a erosividade da chuva em 101 municípios localizados no bioma Cerrado. Para tanto, inicialmente foi realizado o preenchimento de falhas para 81 estações pluviométricas, com série histórica de 20 anos, localizadas nos estados de Mato Grosso, Mato Grosso do Sul, Minas Gerais e Goiás, posteriormente com os dados consistidos, foram aplicadas 16 equações calibradas regionalmente, permitindo determinada a erosividade da chuva. Os resultados apontaram que os municípios localizados na região Nordeste de MS, apresentam os maiores índices El30m, chegando a 2.796,5 MJ mm ha-1 h-1 ano-1 no mês de janeiro, enquanto para o mesmo mês, o menor índice foi de 733,0 MJ mm ha-1 h-1 no Leste de MT. A erosividade da chuva anual ou fator R variou entre 3.713,1 a 12.345,6 MJ mm ha-1 h-1 ano-1, com os menores valores para os municípios da região Leste de MT e o maiores para a região Nordeste de MS. Apesar dos municípios estudados estarem localizados dentro do mesmo bioma, a distribuição espacial lhes confere influências regionais de fatores climáticos diferentes, ocasionando volumes e intensidades pluviométricas distintas, o que reflete no resultado no índice de erosividade mensal e no fator R. Palavras-chave: estações pluviométricas; fator R; preenchimento de falhas; precipitação.

show abstract

Global radiation by simplified models for the state of Mato Grosso, Brazil

Cited by 8 publications

References 27 publications

Global Solar Radiation Models in Minas Gerais, Southeastern Brazil

Global Solar Radiation Models in Minas Gerais, Southeastern Brazil

Imputação De Dados Diários De Radiação Solar Global via Ambiente R

Rainfall Erosivity in Municipalities of the Brazilian Cerrado Biome

Contact Info

Product

Resources

About