{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T18:55:10Z","timestamp":1770749710776,"version":"3.50.0"},"reference-count":61,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2018,10,21]],"date-time":"2018-10-21T00:00:00Z","timestamp":1540080000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["rmets.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Intl Journal of Climatology"],"published-print":{"date-parts":[[2019,7]]},"abstract":"<jats:p>This work analyses three uncertainty sources affecting the observation\u2010based gridded data sets: station density, interpolation methodology and spatial resolution. For this purpose, we consider precipitation in two countries, Poland and Spain, three resolutions (0.11, 0.22 and 0.44\u00b0), three interpolation methods, both areal\u2010 and point\u2010representative implementations, and three different densities of the underlying station network (high\/medium\/low density). As a result, for each resolution and interpolation approach, nine different grids have been obtained for each country and inter\u2010compared using a variance decomposition methodology.<\/jats:p><jats:p>Results indicate larger differences among the data sets for Spain than for Poland, mainly due to the larger spatial variability and complex orography of the former region. The variance decomposition points out to station density as the most influential factor, independent of the season, the areal\u2010 or point\u2010representative implementation and the country considered, and slightly increasing with the spatial resolution. In contrast, the decomposition is stable when extreme precipitation indices are considered, in particular for the 50\u2010year return value.<\/jats:p><jats:p>Finally, the uncertainty due to station sub\u2010sampling inside a particular grid box decreases with the number of stations used in the averaging\/interpolation. In the case of spatially homogeneous grid boxes, the interpolation approach obtains similar results for all the parameters, excepting the wet day frequency, independently of the number of stations. When there is a more significant internal variability in the grid box, the interpolation is more sensitive to the number of stations, pointing out to a minimum stations\u2019 density for the target resolution (six to seven stations).<\/jats:p>","DOI":"10.1002\/joc.5878","type":"journal-article","created":{"date-parts":[[2018,10,5]],"date-time":"2018-10-05T20:28:34Z","timestamp":1538771314000},"page":"3717-3729","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":88,"title":["Uncertainty in gridded precipitation products: Influence of station density, interpolation method and grid resolution"],"prefix":"10.1002","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5384-179X","authenticated-orcid":false,"given":"Sixto","family":"Herrera","sequence":"first","affiliation":[{"name":"Meteorology Group, Departamento de Matem\u00e1tica Aplicada y Ciencias de la Computaci\u00f3n University of Cantabria  Santander Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9542-6781","authenticated-orcid":false,"given":"Sven","family":"Kotlarski","sequence":"additional","affiliation":[{"name":"Federal Office of Meteorology and Climatology MeteoSwiss  Z\u00fcrich Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9155-5874","authenticated-orcid":false,"given":"Pedro M. M.","family":"Soares","sequence":"additional","affiliation":[{"name":"Instituto Dom Luiz (IDL), Faculdade de Ci\u00eancias Universidade de Lisboa  Lisboa Portugal"}]},{"given":"Rita M.","family":"Cardoso","sequence":"additional","affiliation":[{"name":"Instituto Dom Luiz (IDL), Faculdade de Ci\u00eancias Universidade de Lisboa  Lisboa Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2364-5835","authenticated-orcid":false,"given":"Adam","family":"Jaczewski","sequence":"additional","affiliation":[{"name":"Institute of Meteorology and Water Management \u2010 National Research Institute  Warsaw Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2766-6297","authenticated-orcid":false,"given":"Jos\u00e9 M.","family":"Guti\u00e9rrez","sequence":"additional","affiliation":[{"name":"Meteorology Group Instituto de F\u00edsica de Cantabria, CSIC\u2010University of Cantabria  Santander Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4076-0456","authenticated-orcid":false,"given":"Douglas","family":"Maraun","sequence":"additional","affiliation":[{"name":"Wegener Center for Climate and Global Change University of Graz  Graz Austria"}]}],"member":"311","published-online":{"date-parts":[[2018,10,21]]},"reference":[{"key":"e_1_2_7_2_1","volume-title":"Atlas clim\u00e1tico ib\u00e9rico\/Iberian climate atlas","author":"AEMET","year":"2011"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/joc.3370060607"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.wace.2015.06.003"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/2014JD021478"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/joc.4561"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1029\/2010JD015481"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1175\/JCLI-D-14-00316.1"},{"key":"e_1_2_7_9_1","volume-title":"ROCADA: Romanian daily gridded climatic dataset (1961\u20132013) V1.0","author":"Birsan M.V.","year":"2014"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1175\/2007JCLI1494.1"},{"key":"e_1_2_7_11_1","unstructured":"Chen M. 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