Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Haroldo Fraga de Campos Velho"'
Autor:
Yasmin Uchôa da Silva, Gutemberg Borges França, Heloisa Musetti Ruivo, Haroldo Fraga de Campos Velho
Publikováno v:
Applied Computing and Geosciences, Vol 16, Iss , Pp 100099- (2022)
This presents a novel hybrid 24-h forecasting model of convective weather events based on numerical simulation and machine learning algorithms. To characterize the convective events, 13-year from 2008 up to 2020 of precipitation data from the main ai
Externí odkaz:
https://doaj.org/article/6e92180e3e554e60a821b52f5020ba20
Autor:
Vinícius Albuquerque de Almeida, Gutemberg Borges França, Haroldo Fraga de Campos Velho, Nelson Francisco Favilla Ebecken
Publikováno v:
Revista Brasileira de Meteorologia, Vol 36, Iss 1, Pp 87-96 (2021)
Abstract The impact of the data assimilation process of air temperature and relative humidity from surface meteorological stations and sounding at airports in the terminal area of Rio de Janeiro is evaluated using the Weather Research and Forecast Da
Externí odkaz:
https://doaj.org/article/efbb7839d6e74057991d3f72dd524b78
Publikováno v:
Remote Sensing, Vol 14, Iss 2, p 361 (2022)
In this paper we post-process and evaluate the position estimation of pairs of template windows and geo-referenced images generated from LiDAR cloud point data using the Normalized Cross-Correlation (NCC) method. We created intensity, surface and ter
Externí odkaz:
https://doaj.org/article/2334f80b22104380b2c3f7ac819f4bb3
Publikováno v:
Atmosphere, Vol 13, Iss 2, p 243 (2022)
Machine learning has experienced great success in many applications. Precipitation is a hard meteorological variable to predict, but it has a strong impact on society. Here, a machine-learning technique—a formulation of gradient-boosted trees—is
Externí odkaz:
https://doaj.org/article/279c682469ae43a7b417484e0d3e5678
Publikováno v:
Remote Sensing, Vol 13, Iss 13, p 2468 (2021)
Many natural disasters in South America are linked to meteorological phenomena. Therefore, forecasting and monitoring climatic events are fundamental issues for society and various sectors of the economy. In the last decades, machine learning models
Externí odkaz:
https://doaj.org/article/0020586ba1304537be0189b0c9e4bb99
Autor:
Gisele Cristina Rubert, Débora Regina Roberti, Luis Santos Pereira, Fernando L.F. Quadros, Haroldo Fraga de Campos Velho, Osvaldo Luiz Leal de Moraes
Publikováno v:
Water, Vol 11, Iss 9, p 1890 (2019)
The authors wish to make the following corrections to this paper [...]
Externí odkaz:
https://doaj.org/article/1c51846a16254767ae7fbccea9ee8980
Autor:
Gisele Cristina Rubert, Débora Regina Roberti, Luis Santos Pereira, Fernando L. F. Quadros, Haroldo Fraga de Campos Velho, Osvaldo Luiz Leal de Moraes
Publikováno v:
Water, Vol 10, Iss 12, p 1864 (2018)
Experimentally characterizing evapotranspiration (ET) in different biomes around the world is an issue of interest for different areas of science. ET in natural areas of the Brazilian Pampa biome has still not been assessed. In this study, the actual
Externí odkaz:
https://doaj.org/article/b3026cb126fa4a71aa793b8640d7e67b
Autor:
Gutemberg Borges França, Vinícius Albuquerque de Almeida, Leonardo F. Peres, Andrews José de Lucena, Haroldo Fraga de Campos Velho, Manoel Valdonel de Almeida, Gilberto Gomes Pimentel, Karine do Nascimento Cardozo, Liz Barreto Coelho Belém, Vitor Fonseca Vieira Vasconce de Miranda, Leonardo de Brito Ferreira, Álvaro de Souza Andrade Maciel, Fillipi Archetti dos Santos
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0bd47f34b9b8696fc9423a3a203b49d2
https://doi.org/10.2139/ssrn.4343209
https://doi.org/10.2139/ssrn.4343209
Autor:
Vinícius Albuquerque de Almeida, Haroldo Fraga de Campos Velho, Gutemberg Borges França, Nelson Francisco Favilla Ebecken
The practical feasibility of neural networks models for data assimilation using local observations data in the WRF model for the Rio de Janeiro metropolitan region in Brazil is evaluated. Surface and multi-level variables retrieved from airport meteo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::747bfbdf90e407d099ad69e58818cf2c
https://gmd.copernicus.org/preprints/gmd-2022-50/
https://gmd.copernicus.org/preprints/gmd-2022-50/
Publikováno v:
Remote Sensing; Volume 14; Issue 2; Pages: 361
Remote Sensing, Vol 14, Iss 361, p 361 (2022)
Remote Sensing, Vol 14, Iss 361, p 361 (2022)
In this paper we post-process and evaluate the position estimation of pairs of template windows and geo-referenced images generated from LiDAR cloud point data using the Normalized Cross-Correlation (NCC) method. We created intensity, surface and ter
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cf747b42000f51ea5edb3b34885ab500