2026-04-16T19:17:56Zhttps://repository.udca.edu.co/server/oai/request

oai:repository.udca.edu.co:11158/54462024-05-09T19:43:46Zcom_11158_1828com_11158_1085col_11158_1830

Blanco Mantilla, Kevin Villamizar, Sandra Avila Diaz, Alvaro Javier Marcelo, Catalina Santamaria, Erika Lesmes, Maria Camila Cauca, Colombia 2023-09-25T16:41:14Z 2023-09-25T16:41:14Z 2023 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 https://repository.udca.edu.co/handle/11158/5446 https://doi.org/10.1016/j.dib.2023.109542 23523409 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 Incluye referencias bibliográficas application/pdf eng https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es https://creativecommons.org/licenses/by-nc-sa/4.0/ info:eu-repo/semantics/openAccess Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0) https://www.sciencedirect.com/science/article/pii/S235234092300642X Daily dataset of precipitation and temperature in the Department of Cauca, Colombia Artículo de revista http://purl.org/coar/resource_type/c_2df8fbb1 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Text http://purl.org/redcol/resource_type/ART http://purl.org/coar/version/c_970fb48d4fbd8a85 Bases de datos Análisis espacial Indicadores de Salud CHIRPS ERA5-Land MSWX Pronóstico del tiempo (Oct., 2023) Artículo número 109542 14 1 50 Data in Brief http://purl.org/coar/access_right/c_abf2 Publicationopen.access