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...

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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
Description
Summary: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