000127084 001__ 127084
000127084 005__ 20240720100837.0
000127084 0247_ $$2doi$$a10.5194/essd-15-2547-2023
000127084 0248_ $$2sideral$$a134491
000127084 037__ $$aART-2023-134491
000127084 041__ $$aeng
000127084 100__ $$aBeguería, Santiago
000127084 245__ $$aMOPREDAScentury: a long-term monthly precipitation grid for the Spanish mainland
000127084 260__ $$c2023
000127084 5060_ $$aAccess copy available to the general public$$fUnrestricted
000127084 5203_ $$aThis 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).
000127084 536__ $$9info:eu-repo/grantAgreement/ES/MICINN/PID2020-116860RB-C22$$9info:eu-repo/grantAgreement/ES/MINECO-FEDER/CGL2017-83866-C3-3-R
000127084 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000127084 590__ $$a11.2$$b2023
000127084 592__ $$a4.231$$b2023
000127084 591__ $$aMETEOROLOGY & ATMOSPHERIC SCIENCES$$b2 / 110 = 0.018$$c2023$$dQ1$$eT1
000127084 593__ $$aEarth and Planetary Sciences (miscellaneous)$$c2023$$dQ1
000127084 591__ $$aGEOSCIENCES, MULTIDISCIPLINARY$$b4 / 253 = 0.016$$c2023$$dQ1$$eT1
000127084 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000127084 700__ $$0(orcid)0000-0001-5333-2285$$aPeña-Angulo, Dhais$$uUniversidad de Zaragoza
000127084 700__ $$aTrullenque-Blanco, Víctor$$uUniversidad de Zaragoza
000127084 700__ $$0(orcid)0000-0002-8518-9177$$aGonzález-Hidalgo, Carlos$$uUniversidad de Zaragoza
000127084 7102_ $$13006$$2430$$aUniversidad de Zaragoza$$bDpto. Geograf. Ordenac.Territ.$$cÁrea Geografía Física
000127084 773__ $$g15, 6 (2023), 2547-2575$$pEarth syst. sci. data$$tEARTH SYSTEM SCIENCE DATA$$x1866-3508
000127084 8564_ $$s12131379$$uhttps://zaguan.unizar.es/record/127084/files/texto_completo.pdf$$yVersión publicada
000127084 8564_ $$s2566232$$uhttps://zaguan.unizar.es/record/127084/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000127084 909CO $$ooai:zaguan.unizar.es:127084$$particulos$$pdriver
000127084 951__ $$a2024-07-19-18:45:02
000127084 980__ $$aARTICLE