Spatiotemporal forecast with local temporal drift applied to weather patterns in Patagonia
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/s42452-020-2814-0 http://hdl.handle.net/11449/196946 |
Resumo: | Geostatistics was developed to generate maps or 3D models interpolating observed values in space. The so-called spatiotemporal geostatistic applies the same principles to estimate observed values that have both spatial and temporal distribution. Moreover, time series analysis can decompose and extrapolate its main trends and seasonality, preparing data for geostatistical assumptions. Using this principle, this study aims to decompose the time series of a spatiotemporal dataset as external drifts and estimate its residuals by spatiotemporal kriging. Since each observation point is a time series, it is possible to decompose its trend and seasonality locally and map its parameters, preferable, by traditional geostatistics. Aftermath, it is possible to extrapolate the trend and seasonality at each pixel. This procedure can achieve great long-term forecasting maps even in regions with poor sampling due to its time series analysis. As well as, the geostatistics guarantee that the spatio-temporal correlation is maintained. This method is especially good for prediction in regions that the time series pattern depends on its location, which is a common problem in large areas and the problem is worsened in poorly sampled regions. This study presents a 10 years map forecast (2008-2017) comparison by spatiotemporal geostatistics, the first with original data, with ARIMA Models Panels, then with global decomposition, finally, with the local decomposition approach. The target variable is temperature captured by the 18 active weather stations in Patagonia between 1973 and 2007. To validate the results, they are compared to Land Surface Temperature (LST), which is an image product MOD11C3 derived from the MODIS sensor onboard on Terra/Aqua satellites. The proposed method can make long-term forecasts with low error, low smoothing effect and similar spatiotemporal statistics (mean and variance) of the stations and the LST product. Finally, its results are comparable with the ARIMA Models Panels with the advantage that it can generate maps with spatiotemporal correlation and better than the often-used methods (stkriging and global decomposition) to forecast large areas maps. |
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Spatiotemporal forecast with local temporal drift applied to weather patterns in PatagoniaTime series analysisLocal decompositionSpace-time geostatisticsMap forecastingPatagoniaGeostatistics was developed to generate maps or 3D models interpolating observed values in space. The so-called spatiotemporal geostatistic applies the same principles to estimate observed values that have both spatial and temporal distribution. Moreover, time series analysis can decompose and extrapolate its main trends and seasonality, preparing data for geostatistical assumptions. Using this principle, this study aims to decompose the time series of a spatiotemporal dataset as external drifts and estimate its residuals by spatiotemporal kriging. Since each observation point is a time series, it is possible to decompose its trend and seasonality locally and map its parameters, preferable, by traditional geostatistics. Aftermath, it is possible to extrapolate the trend and seasonality at each pixel. This procedure can achieve great long-term forecasting maps even in regions with poor sampling due to its time series analysis. As well as, the geostatistics guarantee that the spatio-temporal correlation is maintained. This method is especially good for prediction in regions that the time series pattern depends on its location, which is a common problem in large areas and the problem is worsened in poorly sampled regions. This study presents a 10 years map forecast (2008-2017) comparison by spatiotemporal geostatistics, the first with original data, with ARIMA Models Panels, then with global decomposition, finally, with the local decomposition approach. The target variable is temperature captured by the 18 active weather stations in Patagonia between 1973 and 2007. To validate the results, they are compared to Land Surface Temperature (LST), which is an image product MOD11C3 derived from the MODIS sensor onboard on Terra/Aqua satellites. The proposed method can make long-term forecasts with low error, low smoothing effect and similar spatiotemporal statistics (mean and variance) of the stations and the LST product. Finally, its results are comparable with the ARIMA Models Panels with the advantage that it can generate maps with spatiotemporal correlation and better than the often-used methods (stkriging and global decomposition) to forecast large areas maps.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)IGc/USP (Institute of Geosciences/University of Sao Paulo)Univ Sao Paulo IGc USP, Inst Geosci, Rua Lago,562 Cidade Univ, BR-05508080 Sao Paulo, SP, BrazilSao Paulo State Univ FCE UNESP, Sch Sci & Engn, Dept Biosyst Engn, Rua Domingos da Costa Lopes,780 Jd Itaipu, BR-17602496 Tupa, SP, BrazilSao Paulo State Univ FCE UNESP, Sch Sci & Engn, Dept Biosyst Engn, Rua Domingos da Costa Lopes,780 Jd Itaipu, BR-17602496 Tupa, SP, BrazilSpringerUniversidade de São Paulo (USP)Universidade Estadual Paulista (Unesp)Moraes Takafuji, Eduardo Henrique deRocha, Marcelo Monteiro daManzione, Rodrigo Lilla [UNESP]2020-12-10T20:01:17Z2020-12-10T20:01:17Z2020-06-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article19http://dx.doi.org/10.1007/s42452-020-2814-0Sn Applied Sciences. Cham: Springer International Publishing Ag, v. 2, n. 6, 19 p., 2020.2523-3963http://hdl.handle.net/11449/19694610.1007/s42452-020-2814-0WOS:000538087000005Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengSn Applied Sciencesinfo:eu-repo/semantics/openAccess2024-06-10T14:49:01Zoai:repositorio.unesp.br:11449/196946Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-06-10T14:49:01Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Spatiotemporal forecast with local temporal drift applied to weather patterns in Patagonia |
title |
Spatiotemporal forecast with local temporal drift applied to weather patterns in Patagonia |
spellingShingle |
Spatiotemporal forecast with local temporal drift applied to weather patterns in Patagonia Moraes Takafuji, Eduardo Henrique de Time series analysis Local decomposition Space-time geostatistics Map forecasting Patagonia |
title_short |
Spatiotemporal forecast with local temporal drift applied to weather patterns in Patagonia |
title_full |
Spatiotemporal forecast with local temporal drift applied to weather patterns in Patagonia |
title_fullStr |
Spatiotemporal forecast with local temporal drift applied to weather patterns in Patagonia |
title_full_unstemmed |
Spatiotemporal forecast with local temporal drift applied to weather patterns in Patagonia |
title_sort |
Spatiotemporal forecast with local temporal drift applied to weather patterns in Patagonia |
author |
Moraes Takafuji, Eduardo Henrique de |
author_facet |
Moraes Takafuji, Eduardo Henrique de Rocha, Marcelo Monteiro da Manzione, Rodrigo Lilla [UNESP] |
author_role |
author |
author2 |
Rocha, Marcelo Monteiro da Manzione, Rodrigo Lilla [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade de São Paulo (USP) Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Moraes Takafuji, Eduardo Henrique de Rocha, Marcelo Monteiro da Manzione, Rodrigo Lilla [UNESP] |
dc.subject.por.fl_str_mv |
Time series analysis Local decomposition Space-time geostatistics Map forecasting Patagonia |
topic |
Time series analysis Local decomposition Space-time geostatistics Map forecasting Patagonia |
description |
Geostatistics was developed to generate maps or 3D models interpolating observed values in space. The so-called spatiotemporal geostatistic applies the same principles to estimate observed values that have both spatial and temporal distribution. Moreover, time series analysis can decompose and extrapolate its main trends and seasonality, preparing data for geostatistical assumptions. Using this principle, this study aims to decompose the time series of a spatiotemporal dataset as external drifts and estimate its residuals by spatiotemporal kriging. Since each observation point is a time series, it is possible to decompose its trend and seasonality locally and map its parameters, preferable, by traditional geostatistics. Aftermath, it is possible to extrapolate the trend and seasonality at each pixel. This procedure can achieve great long-term forecasting maps even in regions with poor sampling due to its time series analysis. As well as, the geostatistics guarantee that the spatio-temporal correlation is maintained. This method is especially good for prediction in regions that the time series pattern depends on its location, which is a common problem in large areas and the problem is worsened in poorly sampled regions. This study presents a 10 years map forecast (2008-2017) comparison by spatiotemporal geostatistics, the first with original data, with ARIMA Models Panels, then with global decomposition, finally, with the local decomposition approach. The target variable is temperature captured by the 18 active weather stations in Patagonia between 1973 and 2007. To validate the results, they are compared to Land Surface Temperature (LST), which is an image product MOD11C3 derived from the MODIS sensor onboard on Terra/Aqua satellites. The proposed method can make long-term forecasts with low error, low smoothing effect and similar spatiotemporal statistics (mean and variance) of the stations and the LST product. Finally, its results are comparable with the ARIMA Models Panels with the advantage that it can generate maps with spatiotemporal correlation and better than the often-used methods (stkriging and global decomposition) to forecast large areas maps. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-12-10T20:01:17Z 2020-12-10T20:01:17Z 2020-06-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1007/s42452-020-2814-0 Sn Applied Sciences. Cham: Springer International Publishing Ag, v. 2, n. 6, 19 p., 2020. 2523-3963 http://hdl.handle.net/11449/196946 10.1007/s42452-020-2814-0 WOS:000538087000005 |
url |
http://dx.doi.org/10.1007/s42452-020-2814-0 http://hdl.handle.net/11449/196946 |
identifier_str_mv |
Sn Applied Sciences. Cham: Springer International Publishing Ag, v. 2, n. 6, 19 p., 2020. 2523-3963 10.1007/s42452-020-2814-0 WOS:000538087000005 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Sn Applied Sciences |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
19 |
dc.publisher.none.fl_str_mv |
Springer |
publisher.none.fl_str_mv |
Springer |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
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1803649428770258944 |