Validation of a global satellite rainfall product for real time monitoring of meteorological extremes
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Knowledge AreaIngeniería Hidráulica
SponsorsThis work is the result of a postdoctoral contract funded by Saavedra Fajardo programme (Ref. 20023/SF/16) of the Consejería de Educación y Universidades of CARM (Autonomous Community of Murcia Region), by the Fundación Séneca-Agencia de Ciencia y Tecnología de la Región de Murcia. The support and availability of information from the Center for Hydrometeorology and Remote Sensing of University of California-Irvine (USA), and from Instituto Murciano de Investigación y Desarrollo Agrario y Alimentario (IMIDA) of CARM are also acknowledged.
Bibliographic CitationFulgencio Cánovas-García, Sandra García-Galiano, Negar Karbalaee, "Validation of a global satellite rainfall product for real time monitoring of meteorological extremes," Proc. SPIE 10421, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIX, 1042109 (2 November 2017); doi: 10.1117/12.2278398
Remotely sensed estimated precipitation
Southeast of Spain
Extreme meteorological phenomena
The real time monitoring of storms is important for the management and prevention of flood risks. However, in the southeast of Spain, it seems that the density of the rain gauge network may not be sufficient to adequately characterize the rainfall spatial distribution or the high rainfall intensities that are reached during storms. Satellite precipitation products such as PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Cloud Classification System) could be used to complement the automatic rain gauge networks and so help solve this problem. However, the PERSIANN-CCS product has only recently become available, so its operational validity for areas such as south-eastern Spain is not yet known. In this work, a methodology for the hourly validation of PERSIANN-CCS is presented. We used the rain gauge stations of the SIAM (Sistema de Información Agraria de Murcia) network to study three storms with a very high return period. These ...
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