MOPREDAScentury: a long-term monthly precipitation grid for the Spanish mainland

被引:5
作者
Begueria, Santiago [1 ]
Pena-Angulo, Dhais [2 ]
Trullenque-Blanco, Victor [2 ]
Gonzalez-Hidalgo, Carlos [2 ,3 ]
机构
[1] Consejo Super Invest Cient EEAD CSIC, Estn Expt Aula, Zaragoza 50059, Spain
[2] Univ Zaragoza, Dept Geog & Ordenac Terr, Zaragoza 50009, Spain
[3] Univ Zaragoza, IUCA, Zaragoza 50009, Spain
关键词
CLIMATE; SPAIN; DATASET; LAND; VALIDATION; FREQUENCY; SAFRAN; TREND;
D O I
10.5194/essd-15-2547-2023
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
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 Begueria et al. (2023).
引用
收藏
页码:2547 / 2575
页数:29
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