Condições Típicas De Vento Sobre a Região Marinha Adjacente À Costa Do Espírito Santo

Oliveira, Kyssyanne Samihra Santos; Quaresma, Valéria da Silva

doi:10.5380/abclima.v22i0.51563

Cited by 9 publications

(11 citation statements)

References 27 publications

(40 reference statements)

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“…The variability observed for PC2 was most likely associated with meteorological systems. It is well known that SASA position enables the intensification of trade winds over coastal regions of Brazil during autumn and winter (Oliveira and Silva Quaresma, 2018; Gilliland and Keim, 2018a; Gilliland and Keim, 2018b; Pantoja et al ., 2019), especially over SEB (Silva et al ., 2016). During spring, the SASA influences the transport of humidity from the Atlantic Ocean to the continent and contributes to the maintenance and intensification of the SACZ (Reboita et al ., 2010; Reboita et al ., 2014; Oliveira and Silva Quaresma, 2018; Pantoja et al ., 2019).…”

Section: Resultsmentioning

confidence: 99%

“…The main meteorological systems over SEB are SASA (Oliveira and Silva Quaresma, 2018; Gilliland and Keim, 2018b; Pantoja et al ., 2019), the South American Monsoon Systems (SAMS) (Reboita et al ., 2010; Nielsen et al ., 2016), FS (Reboita et al ., 2010; Campos et al ., 2016; Oliveira et al ., 2018), and the SACZ (Reboita et al ., 2010; Nielsen et al ., 2016; Nielsen et al ., 2019). Weather over Brazilian coastal regions, including over SEB, is strongly influenced by SASA, which changes air pressure at sea level causing significant variability in wind patterns from March to July (Oliveira and Silva Quaresma, 2018; Pantoja et al ., 2019).…”

Section: Resultsmentioning

confidence: 99%

“…In addition, FSs from the South frequently are associated with episodes of SACZ and SASA, mainly during winter (de Morais et al ., 2010; Reboita et al ., 2010; Reboita et al ., 2014; Brito et al ., 2017; Oliveira and Silva Quaresma, 2018). Besides those synoptic systems, mesoscale events also affect wind speed and precipitation over SEB, especially land, sea, and lake breezes (Reboita et al ., 2014; Campos et al ., 2016; Oliveira‐Júnior et al ., 2017; Pantoja 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 studies have focused on metropolitan regions (MR) of state capitals such as Vitória, state of Espírito Santo (Mattiuzzi and Marchioro, 2012; Micalichen and Dias, 2018; Oliveira and Silva Quaresma, 2018), Belo Horizonte, state of Minas Gerais (Bueno et al ., 2011; Micalichen and Dias, 2018), Rio de Janeiro, state of Rio de Janeiro state (Oliveira‐Júnior et al ., 2013; Pimentel et al ., 2014; Oliveira‐Júnior et al ., 2017), and São Paulo, São Paulo state (Oliveira et al ., 2003; Vemado and Pereira Filho, 2016).…”

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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