MOPREDAScentury: a long-term monthly precipitation grid for the Spanish mainland
Beguería, Santiago ; Peña-Angulo, Dhais (Universidad de Zaragoza) ; Trullenque-Blanco, Víctor (Universidad de Zaragoza) ; González-Hidalgo, Carlos (Universidad de Zaragoza)
Resumen: This article describes the development of a monthly precipitation dataset for the Spanish mainland, covering the period between December 1915 and December 2020. The dataset combines ground observational data from the National Climate Data Bank (NCDB) of the Spanish meteorological service (AEMET) and new data rescued from meteorological
yearbooks published prior to 1951 that were never incorporated into the NCDB. The yearbooks' data represented a significant improvement of the dataset, as it almost doubled the number of weather stations available during the first decades of the 20th century, the period when the data were more scarce. The final dataset contains records from
11 312 stations, although the number of stations with data in a given month varies largely between 674 in 1939 and a maximum of 5234 in 1975. Spatial interpolation was used on the resulting dataset to create monthly precipitation grids. The process involved a two-stage process: estimation of the probability of zero precipitation (dry month) and estimation of precipitation magnitude. Interpolation was carried out using universal kriging, using anomalies (ratios with respect to the 1961–2000 monthly climatology) as dependent variables and several geographic variates as independent variables. Cross-validation results showed that the resulting grids are spatially and temporally unbiased, although the mean error and the variance deflation effect are highest during the first decades of the 20th century, when the observational data were more scarce. The dataset is available at https://doi.org/10.20350/digitalCSIC/15136 under an open license and can be cited as Beguería et al. (2023).
Idioma: Inglés
DOI: 10.5194/essd-15-2547-2023
Año: 2023
Publicado en: EARTH SYSTEM SCIENCE DATA 15, 6 (2023), 2547-2575
ISSN: 1866-3508
Factor impacto JCR: 11.2 (2023)
Categ. JCR: METEOROLOGY & ATMOSPHERIC SCIENCES rank: 2 / 110 = 0.018 (2023) - Q1 - T1
Categ. JCR: GEOSCIENCES, MULTIDISCIPLINARY rank: 4 / 253 = 0.016 (2023) - Q1 - T1
Factor impacto CITESCORE: 18.0 - Earth and Planetary Sciences (all) (Q1)
Factor impacto SCIMAGO: 4.231 - Earth and Planetary Sciences (miscellaneous) (Q1)
Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2020-116860RB-C22
Financiación: info:eu-repo/grantAgreement/ES/MINECO-FEDER/CGL2017-83866-C3-3-R
Tipo y forma: Article (Published version)
Área (Departamento): Área Geografía Física (Dpto. Geograf. Ordenac.Territ.)
You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
Exportado de SIDERAL (2024-07-31-09:51:58)
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yearbooks published prior to 1951 that were never incorporated into the NCDB. The yearbooks' data represented a significant improvement of the dataset, as it almost doubled the number of weather stations available during the first decades of the 20th century, the period when the data were more scarce. The final dataset contains records from
11 312 stations, although the number of stations with data in a given month varies largely between 674 in 1939 and a maximum of 5234 in 1975. Spatial interpolation was used on the resulting dataset to create monthly precipitation grids. The process involved a two-stage process: estimation of the probability of zero precipitation (dry month) and estimation of precipitation magnitude. Interpolation was carried out using universal kriging, using anomalies (ratios with respect to the 1961–2000 monthly climatology) as dependent variables and several geographic variates as independent variables. Cross-validation results showed that the resulting grids are spatially and temporally unbiased, although the mean error and the variance deflation effect are highest during the first decades of the 20th century, when the observational data were more scarce. The dataset is available at https://doi.org/10.20350/digitalCSIC/15136 under an open license and can be cited as Beguería et al. (2023).
Idioma: Inglés
DOI: 10.5194/essd-15-2547-2023
Año: 2023
Publicado en: EARTH SYSTEM SCIENCE DATA 15, 6 (2023), 2547-2575
ISSN: 1866-3508
Factor impacto JCR: 11.2 (2023)
Categ. JCR: METEOROLOGY & ATMOSPHERIC SCIENCES rank: 2 / 110 = 0.018 (2023) - Q1 - T1
Categ. JCR: GEOSCIENCES, MULTIDISCIPLINARY rank: 4 / 253 = 0.016 (2023) - Q1 - T1
Factor impacto CITESCORE: 18.0 - Earth and Planetary Sciences (all) (Q1)
Factor impacto SCIMAGO: 4.231 - Earth and Planetary Sciences (miscellaneous) (Q1)
Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2020-116860RB-C22
Financiación: info:eu-repo/grantAgreement/ES/MINECO-FEDER/CGL2017-83866-C3-3-R
Tipo y forma: Article (Published version)
Área (Departamento): Área Geografía Física (Dpto. Geograf. Ordenac.Territ.)
You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. Exportado de SIDERAL (2024-07-31-09:51:58)
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Record created 2023-09-04, last modified 2024-07-31