The impact of SST on the wind and air temperature simulations: a case study for the coastal region of the Rio de Janeiro state

2018 ◽  
Vol 131 (4) ◽  
pp. 1083-1097 ◽  
Author(s):  
Ian Cunha D`Amato Viana Dragaud ◽  
Mauricio Soares da Silva ◽  
Luiz Paulo de Freitas Assad ◽  
Márcio Cataldi ◽  
Luiz Landau ◽  
...  
Keyword(s):  
Air Temperature ◽  
Rio De Janeiro ◽  
Coastal Region ◽  
The Impact ◽  
Janeiro State

scholarly journals Precipitação e temperatura do ar simuladas pelo modelo ETA/CPTEC - HADCM3 para o estado do Rio de Janeiro

2020 ◽  
Vol 13 (5) ◽  
pp. 2037
Author(s):  
FERNANDA DA SILVA PINHEIRO ◽  
EDNALDO OLIVEIRA DOS SANTOS ◽  
GUSTAVO BASTOS LYRA ◽  
GILBERTO FERNANDO FISCH ◽  
HENDERSON SILVA WANDERLEY
Keyword(s):  
Air Temperature ◽  
Performance Index ◽  
Rio De Janeiro ◽  
Climate Model ◽  
Coefficient Of Determination ◽  
Grid Points ◽  
And Performance ◽  
Hadcm3 Model ◽  
Climatic Series ◽  
Janeiro State

O trabalho avaliou simulações de precipitação e temperatura do ar do modelo Eta-CPTEC para o Rio de Janeiro de 1961-1990. Nas simulações, considerou-se resolução espacial de 40 km para uma grade que compreendeu a América do Sul, com o Eta-CPTEC inicializado com o modelo HadCM3. As séries climáticas observadas das variáveis estudadas foram obtidas de estações meteorológicas do INMET distribuídas no estado do Rio de Janeiro. Estas séries foram comparadas com aquelas extraídas dos pontos de grade do modelo mais próximas das estações. Nas avaliações considerou-se o coeficiente de determinação (r²) da regressão linear simples entre dados observados e simulados; o índice de concordância de Willmott (d), o índice de desempenho (c) e a Raiz do Quadrado Médio do Erro (RQME) e seus componentes sistemático (RQMEs) e não sistemático (RQMEu). As simulações de precipitação apresentaram r² menores do que 0,32, o que indicou baixa precisão, enquanto que a exatidão (d) foi superior a 0,50, com exceção de Bangu (0,16). A baixa precisão comprometeu o desempenho (c) das simulações, com 0,07 <= c <= 0,42, classificados entre “péssimo” e “ruim”. A RQME variou entre 76,2 e 133,4 mm, que correspondeu a um erro de 78,1 e 115,5% em relação à precipitação média. As simulações de temperatura do ar mostraram desempenho melhor do que a precipitação, com maior precisão (0,39 <= r² <= 0,53), exatidão (0,50 <= d <= 0,79) e desempenho (0,36 <= c <= 0,52). A RQME ficou entre 1,9 e 5,7oC, representando 9 e 26% da média da temperatura do ar. Na maior parte das estações, o RQMEs se sobressaiu em relação ao RQMEu, indicando que as simulações podem ser corrigidas usando técnicas estatísticas. Precipitation and air temperature numerical simulations through ETA/CPTEC - HADCM3 model in Rio de Janeiro A B S T R A C TThe present study evaluated the precipitation and air temperature simulations of the Eta-CPTEC model for Rio de Janeiro state in 1961-1990. In the simulations, a spatial resolution of 40 km was considered for a grid that comprised South America, with Eta-CPTEC initialized with HadCM3 model. The observed climatic series of the studied variables were obtained from INMET meteorological stations distributed at Rio de Janeiro state. These series were compared with those extracted from the grid points of the model near to the stations. The coefficient of determination (r²) of the simple linear regression between observed and simulated, Willmott's index of agreement (d), performance index (c), Root Mean Square Error (RQME) and their systematic (RQMEs) and unsystematic (RQMEu) components were considered in the evaluations. The precipitation simulations showed r² less than 0.32, which indicated low precision, while the accuracy (Willmott's d) was greater than 0.50, except for Bangu station. The low precision compromised the performance (index “c”) of the simulations, with 0,07 £ c £ 0,42, classified as "terrible" and "bad". The RQME varied between 76.2 and 133.4 mm, which corresponded error of 78.1 and 115.5% in relation to mean precipitation. The simulations of air temperature showed better performance than precipitation, with greater precision (0.39 £ r² £  0.53), accuracy (0.50 £ d £ 0.79) and performance (0.36 £ c £ 0.52). The RQME was between 1.9 and 5.7oC, which represented respectively 9 and 26% for average of air temperature. In most stations, RQMEs were higher than the RQMEu, which indicated that simulators can be fitted using statistical techniques.Keywords: climate model, meteorological dataset, downscaling

scholarly journals Encounter rate and behavior of Alouatta guariba clamitans in the Ilha Grande State Park, Rio de Janeiro state, Brazil

2020 ◽  
Vol 37 ◽  
pp. 1-10
Author(s):  
Atilla C. Ferreguetti ◽  
Amanda B. M. de Oliveira ◽  
Bruno C. Pereira ◽  
Ricardo T. Santori ◽  
Lena Geise ◽  
...  
Keyword(s):  
Population Size ◽  
Atlantic Forest ◽  
Yellow Fever ◽  
Rio De Janeiro ◽  
Encounter Rate ◽  
Starting Point ◽  
Alouatta Guariba Clamitans ◽  
And Behavior ◽  
The Impact ◽  
Janeiro State

Alouatta guariba clamitans Cabrera, 1940 is an endemic species of the Atlantic Forest that occurs from south Bahia, Brazil, extending south to the province of Misiones, Argentina. In Rio de Janeiro state, the species was classified as threatened, indicating that attention is needed for the conservation of this taxon. Additionally, an outbreak of yellow fever spread throughout the southeastern states of Brazil from January 2017 until March 2018 seriously threatening Rio de Janeiro populations of the species. Herein, we aimed to provide the first estimates of A. g. clamitans encounter rate, density, and population size in the Ilha Grande State Park (PEIG), which is part of the Atlantic Forest biome of Brazil. Data were collected in two different periods, the first between December 2003 and May 2005, and the second from August 2009 to May 2010, and information on encounter rates and behavior was collected to better understand aspects of species' ecology. The estimated encounter rate in the first period through the distance sampling method was 0.04 ± 0.01 individuals per kilometer. Nine groups were recorded in the second period of the study, with 47 individuals along 3 km. Our estimates of encounter rate, density and population size were low and reinforces the need to initiate species monitoring and assess the impact that yellow fever outbreaks may have on PEIG populations. The results presented here can be a starting point to support future strategic actions for the species, to measure impacts and to the management of the species, and for a conservation program.

Influence of the WRF model and atmospheric reanalysis on the offshore wind resource potential and cost estimation: A case study for Rio de Janeiro State

Energy ◽  
2021 ◽  
pp. 122767
Author(s):  
Luiz Filipe de Assis Tavares ◽  
Milad Shadman ◽  
Luiz Paulo de Freitas Assad ◽  
Segen F. Estefen
Keyword(s):  
Cost Estimation ◽  
Rio De Janeiro ◽  
Wrf Model ◽  
Offshore Wind ◽  
Resource Potential ◽  
Wind Resource ◽  
Janeiro State

scholarly journals Assessment of the Demarcation Method for Federal Riverine and Accreted Lands: Case Study of the Rio De Janeiro State Section of the South Paraíba River

2019 ◽  
Vol 11 (11) ◽  
pp. 1313-1326
Author(s):  
Jussara Stutz Oliveira ◽  
Mônica de Aquino Galeano Massera da Hora ◽  
Marcos Aurélio V. de Freitas
Keyword(s):  
Rio De Janeiro ◽  
The South ◽  
Janeiro State
Sign in / Sign up

Export Citation Format

Share Document