Daily dataset of precipitation and temperature in the Department of Cauca, Colombia

This study used the geostatistical Kriging methodology to reduce the spatial scale of a host of daily eteorological variables in the Department of Cauca (Colombia), namely, total precipitation and maximum, minimum, and average temperature. The objective was to supply a high-resolution database from...

Full description

Autores:
Blanco Mantilla, Kevin
Villamizar, Sandra
Avila Diaz, Alvaro Javier
Marcelo, Catalina
Santamaria, Erika
Lesmes, Maria Camila
Tipo de recurso:
Article of investigation
Fecha de publicación:
2023
Institución:
Universidad de Ciencias Aplicadas y Ambientales U.D.C.A
Repositorio:
Repositorio Institucional UDCA
Idioma:
eng
OAI Identifier:
oai:repository.udca.edu.co:11158/5446
Acceso en línea:
https://repository.udca.edu.co/handle/11158/5446
https://doi.org/10.1016/j.dib.2023.109542
Palabra clave:
Bases de datos
Análisis espacial
Indicadores de Salud
CHIRPS
ERA5-Land
MSWX
Pronóstico del tiempo
Rights
openAccess
License
https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es
id RepoUDCA2_8d1279ac4bf4b592dd5f56660e8fc202
oai_identifier_str oai:repository.udca.edu.co:11158/5446
network_acronym_str RepoUDCA2
network_name_str Repositorio Institucional UDCA
repository_id_str
dc.title.eng.fl_str_mv Daily dataset of precipitation and temperature in the Department of Cauca, Colombia
title Daily dataset of precipitation and temperature in the Department of Cauca, Colombia
spellingShingle Daily dataset of precipitation and temperature in the Department of Cauca, Colombia
Bases de datos
Análisis espacial
Indicadores de Salud
CHIRPS
ERA5-Land
MSWX
Pronóstico del tiempo
title_short Daily dataset of precipitation and temperature in the Department of Cauca, Colombia
title_full Daily dataset of precipitation and temperature in the Department of Cauca, Colombia
title_fullStr Daily dataset of precipitation and temperature in the Department of Cauca, Colombia
title_full_unstemmed Daily dataset of precipitation and temperature in the Department of Cauca, Colombia
title_sort Daily dataset of precipitation and temperature in the Department of Cauca, Colombia
dc.creator.fl_str_mv Blanco Mantilla, Kevin
Villamizar, Sandra
Avila Diaz, Alvaro Javier
Marcelo, Catalina
Santamaria, Erika
Lesmes, Maria Camila
dc.contributor.author.none.fl_str_mv Blanco Mantilla, Kevin
Villamizar, Sandra
Avila Diaz, Alvaro Javier
Marcelo, Catalina
Santamaria, Erika
Lesmes, Maria Camila
dc.subject.lemb.none.fl_str_mv Bases de datos
topic Bases de datos
Análisis espacial
Indicadores de Salud
CHIRPS
ERA5-Land
MSWX
Pronóstico del tiempo
dc.subject.lemb.spa.fl_str_mv Análisis espacial
dc.subject.decs.none.fl_str_mv Indicadores de Salud
dc.subject.proposal.spa.fl_str_mv CHIRPS
dc.subject.proposal.eng.fl_str_mv ERA5-Land
MSWX
dc.subject.agrovoc.none.fl_str_mv Pronóstico del tiempo
description This study used the geostatistical Kriging methodology to reduce the spatial scale of a host of daily eteorological variables in the Department of Cauca (Colombia), namely, total precipitation and maximum, minimum, and average temperature. The objective was to supply a high-resolution database from 01/01/2015 to 31/12/2021 in order to support the climate component in a project led by the National Institute of Health (INS) named “Spatial Stratification of dengue based on the identification of risk factors: a pilot study in the Department of Cauca”. The scaling process was applied to available databases from satellite information and reanalysis sources, specifically, CHIRPS (Climate Hazards Group InfraRed Precipitation with Station Data), ERA5-Land (European Centre for Medium-Range Weather Forecasts), and MSWX (Multi-Source Weather). The 0.1° resolution offered by both the MSWX and ERA5-Land databases and the 0.05° resolution found in CHIRPS, was successfully reduced to a scale of 0.01° across all variables. Statistical metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Person Correlation Coefficient (r), and Mean Bias Error (MBE) were used to select the database that best estimated each variable. As a result, it was determined that the scaled ERA5-Land database yielded the best performance for precipitation and minimum daily temperature. On the other hand, the scaled MSWX database showed the best behavior for the other two variables of maximum temperature and daily average temperature. Additionally, using the scaled meteorological databases improved the performance of the regression models implemented by the INS for constructing a dengue early warning system
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2023-09-25T16:41:14Z
dc.date.available.none.fl_str_mv 2023-09-25T16:41:14Z
dc.date.issued.none.fl_str_mv 2023
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.content.spa.fl_str_mv Text
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/ART
dc.type.coarversion.spa.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
format http://purl.org/coar/resource_type/c_2df8fbb1
status_str publishedVersion
dc.identifier.citation.spa.fl_str_mv Blanco, K., Villamizar, S.R., Avila-Diaz, A., Marceló-Díaz, C., Santamaría, E., Lesmes, M.C. Daily dataset of precipitation and temperature in the Department of Cauca, Colombia (2023) Data in Brief, 50, art. no. 109542 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171477383&doi=10.1016%2fj.dib.2023.109542&partnerID=40&md5=2DOI: 10.1016/j.dib.2023.109542
dc.identifier.uri.none.fl_str_mv https://repository.udca.edu.co/handle/11158/5446
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1016/j.dib.2023.109542
dc.identifier.eissn.spa.fl_str_mv 23523409
identifier_str_mv Blanco, K., Villamizar, S.R., Avila-Diaz, A., Marceló-Díaz, C., Santamaría, E., Lesmes, M.C. Daily dataset of precipitation and temperature in the Department of Cauca, Colombia (2023) Data in Brief, 50, art. no. 109542 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171477383&doi=10.1016%2fj.dib.2023.109542&partnerID=40&md5=2DOI: 10.1016/j.dib.2023.109542
23523409
url https://repository.udca.edu.co/handle/11158/5446
https://doi.org/10.1016/j.dib.2023.109542
dc.language.iso.spa.fl_str_mv eng
language eng
dc.relation.citationedition.spa.fl_str_mv (Oct., 2023) Artículo número 109542
dc.relation.citationendpage.spa.fl_str_mv 14
dc.relation.citationstartpage.spa.fl_str_mv 1
dc.relation.citationvolume.spa.fl_str_mv 50
dc.relation.ispartofjournal.spa.fl_str_mv Data in Brief
dc.rights.spa.fl_str_mv https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.uri.spa.fl_str_mv https://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.license.spa.fl_str_mv Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es
https://creativecommons.org/licenses/by-nc-sa/4.0/
Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.coverage.spatial.spa.fl_str_mv Cauca, Colombia
dc.source.spa.fl_str_mv https://www.sciencedirect.com/science/article/pii/S235234092300642X
institution Universidad de Ciencias Aplicadas y Ambientales U.D.C.A
bitstream.url.fl_str_mv https://repository.udca.edu.co/bitstreams/4e87926c-dc36-4b0c-85df-0d5e7759e18b/download
https://repository.udca.edu.co/bitstreams/863aa00b-0bc7-41cb-a273-302427d8992e/download
https://repository.udca.edu.co/bitstreams/9b239c10-e820-4b52-8e94-1c49a9f8f599/download
https://repository.udca.edu.co/bitstreams/6043aea4-d06c-425f-89bf-d628206b233d/download
bitstream.checksum.fl_str_mv 7a51e44d8ef8cd3bc772e55fba49bc9c
f661acf14bedbf9f5d13897a0387e751
2ad023eebf51a500b5ad98f5149d8f1c
81b7ca2b95ab174896f09b7b34e4cd46
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
repository.name.fl_str_mv Repositorio - Universidad de Ciencias Aplicadas y Ambientales UDCA.
repository.mail.fl_str_mv bdigital@metabiblioteca.com
_version_ 1814213427616284672
spelling Blanco Mantilla, KevinVillamizar, SandraAvila Diaz, Alvaro JavierMarcelo, CatalinaSantamaria, ErikaLesmes, Maria CamilaCauca, Colombia2023-09-25T16:41:14Z2023-09-25T16:41:14Z2023Blanco, K., Villamizar, S.R., Avila-Diaz, A., Marceló-Díaz, C., Santamaría, E., Lesmes, M.C. Daily dataset of precipitation and temperature in the Department of Cauca, Colombia (2023) Data in Brief, 50, art. no. 109542 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171477383&doi=10.1016%2fj.dib.2023.109542&partnerID=40&md5=2DOI: 10.1016/j.dib.2023.109542https://repository.udca.edu.co/handle/11158/5446https://doi.org/10.1016/j.dib.2023.10954223523409This study used the geostatistical Kriging methodology to reduce the spatial scale of a host of daily eteorological variables in the Department of Cauca (Colombia), namely, total precipitation and maximum, minimum, and average temperature. The objective was to supply a high-resolution database from 01/01/2015 to 31/12/2021 in order to support the climate component in a project led by the National Institute of Health (INS) named “Spatial Stratification of dengue based on the identification of risk factors: a pilot study in the Department of Cauca”. The scaling process was applied to available databases from satellite information and reanalysis sources, specifically, CHIRPS (Climate Hazards Group InfraRed Precipitation with Station Data), ERA5-Land (European Centre for Medium-Range Weather Forecasts), and MSWX (Multi-Source Weather). The 0.1° resolution offered by both the MSWX and ERA5-Land databases and the 0.05° resolution found in CHIRPS, was successfully reduced to a scale of 0.01° across all variables. Statistical metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Person Correlation Coefficient (r), and Mean Bias Error (MBE) were used to select the database that best estimated each variable. As a result, it was determined that the scaled ERA5-Land database yielded the best performance for precipitation and minimum daily temperature. On the other hand, the scaled MSWX database showed the best behavior for the other two variables of maximum temperature and daily average temperature. Additionally, using the scaled meteorological databases improved the performance of the regression models implemented by the INS for constructing a dengue early warning systemIncluye referencias bibliográficasapplication/pdfenghttps://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.eshttps://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)http://purl.org/coar/access_right/c_abf2https://www.sciencedirect.com/science/article/pii/S235234092300642XDaily dataset of precipitation and temperature in the Department of Cauca, ColombiaArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionTexthttp://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85Bases de datosAnálisis espacialIndicadores de SaludCHIRPSERA5-LandMSWXPronóstico del tiempo(Oct., 2023) Artículo número 10954214150Data in BriefPublicationORIGINALDaily.pdfDaily.pdfapplication/pdf5412718https://repository.udca.edu.co/bitstreams/4e87926c-dc36-4b0c-85df-0d5e7759e18b/download7a51e44d8ef8cd3bc772e55fba49bc9cMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-814775https://repository.udca.edu.co/bitstreams/863aa00b-0bc7-41cb-a273-302427d8992e/downloadf661acf14bedbf9f5d13897a0387e751MD52TEXTDaily.pdf.txtDaily.pdf.txtExtracted texttext/plain23193https://repository.udca.edu.co/bitstreams/9b239c10-e820-4b52-8e94-1c49a9f8f599/download2ad023eebf51a500b5ad98f5149d8f1cMD53THUMBNAILDaily.pdf.jpgDaily.pdf.jpgGenerated Thumbnailimage/jpeg12661https://repository.udca.edu.co/bitstreams/6043aea4-d06c-425f-89bf-d628206b233d/download81b7ca2b95ab174896f09b7b34e4cd46MD5411158/5446oai:repository.udca.edu.co:11158/54462024-05-09 14:43:46.151https://creativecommons.org/licenses/by-nc-sa/4.0/https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.esopen.accesshttps://repository.udca.edu.coRepositorio - Universidad de Ciencias Aplicadas y Ambientales UDCA.bdigital@metabiblioteca.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