{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:33:13Z","timestamp":1775665993008,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2018,6,27]],"date-time":"2018-06-27T00:00:00Z","timestamp":1530057600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007801","name":"Fundaci\u00f3n S\u00e9neca","doi-asserted-by":"publisher","award":["20023\/SF\/16"],"award-info":[{"award-number":["20023\/SF\/16"]}],"id":[{"id":"10.13039\/100007801","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Quantitative Precipitation Estimates (QPEs) obtained from remote sensing or ground-based radars could complement or even be an alternative to rain gauge readings. However, to be used in operational applications, a validation process has to be carried out, usually by comparing their estimates with those of a rain gauges network. In this paper, the accuracy of three QPEs are evaluated for three extreme precipitation events in the last decade in the southeast of the Iberian Peninsula. The first QPE is PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Cloud Classification System) , a satellite-based QPE. The second and the third are QPEs from a meteorological radar with Doppler capabilities that works in the C band. Pixel-to-point comparisons are made between the values offered by the QPEs and those obtained by two networks of rain gauges. The results obtained indicate that all the QPEs were well below the rain gauge values in extreme rainfall time slots. There seems to be a weak linear association between the value of the discrepancies and the precipitation value of the QPEs. The main conclusion, assuming the information from the rain gauges as ground truth, is that neither PERSIANN-CCS nor radar, without empirical calibration, are acceptable QPEs for the real-time monitoring of meteorological extremes in the southeast of the Iberian Peninsula.<\/jats:p>","DOI":"10.3390\/rs10071023","type":"journal-article","created":{"date-parts":[[2018,6,27]],"date-time":"2018-06-27T11:02:05Z","timestamp":1530097325000},"page":"1023","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Assessment of Satellite and Radar Quantitative Precipitation Estimates for Real Time Monitoring of Meteorological Extremes Over the Southeast of the Iberian Peninsula"],"prefix":"10.3390","volume":"10","author":[{"given":"Fulgencio","family":"C\u00e1novas-Garc\u00eda","sequence":"first","affiliation":[{"name":"Unidad Predepartamental de Ingenier\u00eda Civil, Universidad Polit\u00e9cnica de Cartagena, Paseo Alfonso XIII, 52, 30203 Cartagena, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3557-3266","authenticated-orcid":false,"given":"Sandra","family":"Garc\u00eda-Galiano","sequence":"additional","affiliation":[{"name":"Unidad Predepartamental de Ingenier\u00eda Civil, Universidad Polit\u00e9cnica de Cartagena, Paseo Alfonso XIII, 52, 30203 Cartagena, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0466-5184","authenticated-orcid":false,"given":"Francisco","family":"Alonso-Sarr\u00eda","sequence":"additional","affiliation":[{"name":"Instituto Universitario del Agua y del Medio Ambiente, Universidad de Murcia, Edificio D, Campus de Espinardo, s\/n, 30100 Murcia, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2018,6,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1834","DOI":"10.1175\/JAM2173.1","article-title":"Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System","volume":"43","author":"Hong","year":"2004","journal-title":"J. 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