Variogram as a tool for assessing the quality of climate models
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Revista Interdisciplinar de Pesquisa em Engenharia |
Texto Completo: | https://periodicos.unb.br/index.php/ripe/article/view/21612 |
Resumo: | Climate models are very sensitive to spatial resolution. Their skill must always be verified, as they involve several phenomena which take place in different scales. For that reason, some of those phenomena must be adequately parameterized, with appropriate techniques of upscaling. The proposal of this work is to present the variogram as a tool for assessing the quality of climate models, based on comparison of model results with different spatial discretization. Results of the ETA Model from INPE are presented in two different levels of discretisation: for resolutions higher than 5 km, to which non-hydrostatic models must be taken into account, and for resolution lower than 8 km, to which hydrostatic models are suited. Variograms for 36 km, 18 km, 4 km, 2 km and 1 km are calculated and their results are discussed, together with other metrics for quality assessment of forecast models. Variograms showed that there is an impact of grid coarseness over these numerical models, which was less noticeable in plots of precipitations for coarser grids. |
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Variogram as a tool for assessing the quality of climate modelsVariogram. Geostatistics. Climate model. Model quality assessment.Climate models are very sensitive to spatial resolution. Their skill must always be verified, as they involve several phenomena which take place in different scales. For that reason, some of those phenomena must be adequately parameterized, with appropriate techniques of upscaling. The proposal of this work is to present the variogram as a tool for assessing the quality of climate models, based on comparison of model results with different spatial discretization. Results of the ETA Model from INPE are presented in two different levels of discretisation: for resolutions higher than 5 km, to which non-hydrostatic models must be taken into account, and for resolution lower than 8 km, to which hydrostatic models are suited. Variograms for 36 km, 18 km, 4 km, 2 km and 1 km are calculated and their results are discussed, together with other metrics for quality assessment of forecast models. Variograms showed that there is an impact of grid coarseness over these numerical models, which was less noticeable in plots of precipitations for coarser grids.Programa de Pós-Graduação em Integridade de Materiais da Engenharia2017-01-30info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://periodicos.unb.br/index.php/ripe/article/view/2161210.26512/ripe.v2i16.21612Revista Interdisciplinar de Pesquisa em Engenharia; Vol. 2 No. 16 (2016): STOCHASTIC MODELING AND UNCERTAINTY QUANTIFICATION; 12-22Revista Interdisciplinar de Pesquisa em Engenharia; v. 2 n. 16 (2016): STOCHASTIC MODELING AND UNCERTAINTY QUANTIFICATION; 12-222447-6102reponame:Revista Interdisciplinar de Pesquisa em Engenhariainstname:Universidade de Brasília (UnB)instacron:UNBenghttps://periodicos.unb.br/index.php/ripe/article/view/21612/19930Copyright (c) 2019 Revista Interdisciplinar de Pesquisa em Engenharia - RIPEinfo:eu-repo/semantics/openAccessZanetti, Vitor BaccarinChou, Sin ChanGandini, Maria Luiza TeófiloLyra, André2019-06-16T03:01:28Zoai:ojs.pkp.sfu.ca:article/21612Revistahttps://periodicos.unb.br/index.php/ripePUBhttps://periodicos.unb.br/index.php/ripe/oaianflor@unb.br2447-61022447-6102opendoar:2019-06-16T03:01:28Revista Interdisciplinar de Pesquisa em Engenharia - Universidade de Brasília (UnB)false |
dc.title.none.fl_str_mv |
Variogram as a tool for assessing the quality of climate models |
title |
Variogram as a tool for assessing the quality of climate models |
spellingShingle |
Variogram as a tool for assessing the quality of climate models Zanetti, Vitor Baccarin Variogram. Geostatistics. Climate model. Model quality assessment. |
title_short |
Variogram as a tool for assessing the quality of climate models |
title_full |
Variogram as a tool for assessing the quality of climate models |
title_fullStr |
Variogram as a tool for assessing the quality of climate models |
title_full_unstemmed |
Variogram as a tool for assessing the quality of climate models |
title_sort |
Variogram as a tool for assessing the quality of climate models |
author |
Zanetti, Vitor Baccarin |
author_facet |
Zanetti, Vitor Baccarin Chou, Sin Chan Gandini, Maria Luiza Teófilo Lyra, André |
author_role |
author |
author2 |
Chou, Sin Chan Gandini, Maria Luiza Teófilo Lyra, André |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Zanetti, Vitor Baccarin Chou, Sin Chan Gandini, Maria Luiza Teófilo Lyra, André |
dc.subject.por.fl_str_mv |
Variogram. Geostatistics. Climate model. Model quality assessment. |
topic |
Variogram. Geostatistics. Climate model. Model quality assessment. |
description |
Climate models are very sensitive to spatial resolution. Their skill must always be verified, as they involve several phenomena which take place in different scales. For that reason, some of those phenomena must be adequately parameterized, with appropriate techniques of upscaling. The proposal of this work is to present the variogram as a tool for assessing the quality of climate models, based on comparison of model results with different spatial discretization. Results of the ETA Model from INPE are presented in two different levels of discretisation: for resolutions higher than 5 km, to which non-hydrostatic models must be taken into account, and for resolution lower than 8 km, to which hydrostatic models are suited. Variograms for 36 km, 18 km, 4 km, 2 km and 1 km are calculated and their results are discussed, together with other metrics for quality assessment of forecast models. Variograms showed that there is an impact of grid coarseness over these numerical models, which was less noticeable in plots of precipitations for coarser grids. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-01-30 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://periodicos.unb.br/index.php/ripe/article/view/21612 10.26512/ripe.v2i16.21612 |
url |
https://periodicos.unb.br/index.php/ripe/article/view/21612 |
identifier_str_mv |
10.26512/ripe.v2i16.21612 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://periodicos.unb.br/index.php/ripe/article/view/21612/19930 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2019 Revista Interdisciplinar de Pesquisa em Engenharia - RIPE info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2019 Revista Interdisciplinar de Pesquisa em Engenharia - RIPE |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Programa de Pós-Graduação em Integridade de Materiais da Engenharia |
publisher.none.fl_str_mv |
Programa de Pós-Graduação em Integridade de Materiais da Engenharia |
dc.source.none.fl_str_mv |
Revista Interdisciplinar de Pesquisa em Engenharia; Vol. 2 No. 16 (2016): STOCHASTIC MODELING AND UNCERTAINTY QUANTIFICATION; 12-22 Revista Interdisciplinar de Pesquisa em Engenharia; v. 2 n. 16 (2016): STOCHASTIC MODELING AND UNCERTAINTY QUANTIFICATION; 12-22 2447-6102 reponame:Revista Interdisciplinar de Pesquisa em Engenharia instname:Universidade de Brasília (UnB) instacron:UNB |
instname_str |
Universidade de Brasília (UnB) |
instacron_str |
UNB |
institution |
UNB |
reponame_str |
Revista Interdisciplinar de Pesquisa em Engenharia |
collection |
Revista Interdisciplinar de Pesquisa em Engenharia |
repository.name.fl_str_mv |
Revista Interdisciplinar de Pesquisa em Engenharia - Universidade de Brasília (UnB) |
repository.mail.fl_str_mv |
anflor@unb.br |
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1798315226556268544 |