TY - JOUR
T1 - Daily dataset of precipitation and temperature in the Department of Cauca, Colombia
AU - Blanco, Kevin
AU - Villamizar, Sandra R.
AU - Avila-Diaz, Alvaro
AU - Marceló-Díaz, Catalina
AU - Santamaría, Erika
AU - Lesmes, María Camila
N1 - Funding Information:
Financing: This research was funded by the National Institute of Health, Bogotá, Colombia (research project CEMIN 13-2019 ), the Secretaria de Salud Departamental del Cauca, the Universidad de Ciencias Aplicadas y Ambientales-UDCA and Minciencias (research project 210484467217 ).
Publisher Copyright:
© 2023 The Author(s)
PY - 2023/10
Y1 - 2023/10
N2 - This study used the geostatistical Kriging methodology to reduce the spatial scale of a host of daily meteorological 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.
AB - This study used the geostatistical Kriging methodology to reduce the spatial scale of a host of daily meteorological 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.
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U2 - 10.1016/j.dib.2023.109542
DO - 10.1016/j.dib.2023.109542
M3 - Article
C2 - 37743883
AN - SCOPUS:85171477383
SN - 2352-3409
VL - 50
JO - Data in Brief
JF - Data in Brief
M1 - 109542
ER -
