Investigating the Characteristics and Predictability of Measured Wind Speed Data Over Rio de Janeiro, Brazil

Filho, Washington Luiz Félix Correia; Souza, Pedro Henrique de Almeida; Oliveira‐Júnior, José Francisco de; Terassi, Paulo Miguel de Bodas; Góis, Givanildo de; Silva, Carlos Antônio da; Sobral, Bruno Serafini; Rangel, Rafael Henrique Oliveira; Pimentel, Leonardo Duarte

doi:10.1007/s00024-021-02751-w

Cited by 8 publications

(8 citation statements)

References 60 publications

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“…Group G4 consists of only one station over Arraial do Cabo (RJ), in a coastal lowland with low slope (0°–2°) (Figure 5b). These conditions enhance sea/land breezes (Reboita et al ., 2014; Campos et al ., 2016; Oliveira et al ., 2018; Correia Filho et al ., 2021). Arraial do Cabo has good regularity of the wind regime and appropriate conditions to wind energy production, according to results from a study using the Weibull distribution (Sousa Gomes et al ., 2019).…”

Section: Resultsmentioning

confidence: 99%

“…Frontal systems (FS) also contribute to the occurrence of rainfall extremes and changes in the wind regime over SEB (de Morais et al ., 2010; Reboita et al ., 2010; Grimm, 2011; Andrade and Cavalcanti, 2018; Oliveira and Silva Quaresma, 2018; Sobral et al ., 2018b; Correia Filho et al ., 2021). In addition to the influence of SACZ and FS, the region's wind regime is strongly affected by the South Atlantic Subtropical Anticyclone (SASA) and by Atmospheric Blocking (AB) (Reboita et al ., 2010; Grimm, 2011; Gilliland and Keim, 2018b; Machado et al ., 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Other methods include multivariate tools such as principal components analysis (PCA) (Correia Filho and Silva Aragão, 2014; Geng et al ., 2020) and cluster analysis (Burlando et al ., 2008; Correia Filho and Silva Aragão, 2014; Souza et al ., 2020). Finally, the wind regime can be characterized using artificial neural networks (ANN) (Dong et al ., 2016; Zhang et al ., 2020) and regression models (Abdul‐Wahab et al ., 2005; Gilliland and Keim, 2018b; Cai et al ., 2020; Gilliland et al ., 2020; Correia Filho et al ., 2021).…”

Section: Introductionmentioning

confidence: 99%

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The wind regime over the Brazilian Southeast: Spatial and temporal characterization using multivariate analysis

Filho

Souza

Júnior

et al. 2021

Intl Journal of Climatology

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The characterization of spatial and temporal patterns of wind is essential to several sectors, including energy, urban climate, and applied meteorology. However, few studies describe the regional characteristics of the wind regime over the Brazilian Southeast (SEB), the most developed and populated part of the country. The objectives of the current work were (a) to assess the spatial patterns of the wind regime using cluster analysis (CA) and (b) to apply principal components analysis (PCA) to investigate which meteorological systems influence the spatial and temporal patterns of the wind regime. The dataset consisted of wind speed and direction from 70 automatic weather stations with records from 2008 to 2019. According to the CA method, four groups of homogeneous wind speed (G1–G4) were identified; G4 presented the highest magnitudes of wind speed (wind speed >5 m·s−1, with maxima of 7.2 m·s−1). Seasonal well‐defined minima (March–June) and maxima (July–October) were observed only for G2 and G3. The systems South Atlantic Subtropical Anticyclone (SASA), Frontal Systems (FS), and the South Atlantic Convergence Zone (SACZ) influenced these groups. In addition, the mesoscale meteorological systems likely influence other groups such as breezes from land/sea (G1, G3, and G4), lakes (G2 and G3), and valley/mountain (G2 and G3). The topography had a strong influence on G3, mainly due to the mountain ranges of Mantiqueira and Serra do Mar and Paraíba River valley. Dominant wind directions were E, N/NE, and NW, associated with SASA, in addition to S and SE/SW, influenced by FS and SACZ. The wind speed range of 1.4–3.9 m·s−1 was dominant in all groups, except for G4 (range of 2.4–5.5 m·s−1). According to PCA analysis, two PCs are enough to explain the wind pattern in the SEB with 69.9% of the total variance explained. The surface roughness accounted for 29% of the total variance explained in PC1. The latitude (37.6%) and distance to the coast (30.8%) are the most important variables for PC2. Region's physiography and meteorological systems strongly influenced the wind regime over the Brazilian Southeast. The assessment of wind regime variability presented in this work is expected to support public policies on renewable energy applications, air pollution, and climate change mitigation.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The wind regime over the Brazilian Southeast: Spatial and temporal characterization using multivariate analysis

Filho

Souza

Júnior

et al. 2021

Intl Journal of Climatology

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“…Displacement of synoptic systems occurs in the side from Serra do Mar directed toward the interior of the state with North-Northeast direction (N/NE). Médio Paraíba region is influenced by the same systems, but the displacement occurs on the side of the Mantiqueira directed toward Southwest-Northwest portions (SW/NW) [7,33,34] .…”

Section: Discussionmentioning

confidence: 99%

“…However, stations in different regions of the state differ in relation to systems and preferred displacement as, for example, stations located at Serrana and Centro-Sul Fluminense regions, which are influenced by valley/mountain breeze circulation, local convection, FS, SACZ, SASH and AB [4,7,11] . The stations located in the Norte and Nordeste Fluminense are influenced by the same local and synoptic scale systems and, except for maritime/terrestrial breeze circulation [7,16,34] . The displacement of the systems at Serrana and Centro-Sul Fluminense regions occurs in the direction North/ Northeast (N/NE) to the side of the Serra do Mar directed to the continent, while the flow for the stations from Norte and Nordeste Fluminense occurs preferably from North/ Northeast/Southeast (N/NE/SE) [7] .…”

Section: Discussionmentioning

confidence: 99%

Processing of Rainfall Time Series Data in the State of Rio de Janeiro

Góis

Oliveira‐Júnior

2021

j. of atmos. sci. res.

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The goal was to perform the filling, consistency and processing of the rainfall time series data from 1943 to 2013 in five regions of the state. Data were obtained from several sources (ANA, CPRM, INMET, SERLA and LIGHT), totaling 23 stations. The time series (raw data) showed failures that were filled with data from TRMM satellite via 3B43 product, and with the climatological normal from INMET. The 3B43 product was used from 1998 to 2013 and the climatological normal over the 1947- 1997 period. Data were submitted to descriptive and exploratory analysis, parametric tests (Shapiro-Wilks and Bartlett), cluster analysis (CA), and data processing (Box Cox) in the 23 stations. Descriptive analysis of the raw data consistency showed a probability of occurrence above 75% (high time variability). Through the CA, two homogeneous rainfall groups (G1 and G2) were defined. The group G1 and G2 represent 77.01% and 22.99% of the rainfall occurring in SRJ, respectively. Box Cox Processing was effective in stabilizing the normality of the residuals and homogeneity of variance of the monthly rainfall time series of the five regions of the state. Data from 3B43 product and the climatological normal can be used as an alternative source of quality data for gap filling.

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Assessment of meteorological settings on air quality modeling system—a proposal for UN-SDG and regulatory studies in non-homogeneous regions in Brazil

Silva

Pimentel

Duda

et al. 2022

Environ Sci Pollut Res

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Air quality models are essential tools to meet the United Nations Sustainable Development Goals (UN-SDG) because they are effective in guiding public policies for the management of air pollutant emissions and their impacts on the environment and human health. Despite its importance, Brazil still lacks a guide for choosing and setting air quality models for regulatory purposes. Based on this, the current research aims to assess the combined WRF/CALMET/CALPUFF models for representing SO2 dispersion over non-homogeneous regions as a regulatory model for policies in Brazilian Metropolitan Regions to satisfy the UN-SDG. The combined system was applied to the Rio de Janeiro Metropolitan Region (RJMR), which is known for its physiographic complexity. In the rst step, the WRF model was evaluated against surface-observed data. The local circulation was underestimated, while the prevailing observational winds were well-represented. In the second step, it was veri ed that all CALMET three meteorological con gurations performed better for the most frequent wind speed classes, so that the largest SO2 concentrations errors occurred during light winds. Among the meteorological settings in WRF/CALMET/CALPUFF, the joined use of observed and modeled meteorological data yielded the best results for the dispersion of pollutants. This result emphasizes the relevance of meteorological data composition in complex regions with unsatisfactory monitoring given the inherent limitations of prognostic models and the excessive extrapolation of observed data that can generate distortions of reality. This research concludes with the proposal of the WRF/CALMET/CALPUFF air quality regulatory system as a supporting tool for policies in the Brazilian Metropolitan Regions in the framework of the UN-SDG, particularly in non-homogeneous regions where steady-state Gaussian models are not applicable.

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Investigating the Characteristics and Predictability of Measured Wind Speed Data Over Rio de Janeiro, Brazil

Cited by 8 publications

References 60 publications

The wind regime over the Brazilian Southeast: Spatial and temporal characterization using multivariate analysis

The wind regime over the Brazilian Southeast: Spatial and temporal characterization using multivariate analysis

Processing of Rainfall Time Series Data in the State of Rio de Janeiro

Assessment of meteorological settings on air quality modeling system—a proposal for UN-SDG and regulatory studies in non-homogeneous regions in Brazil

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