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Improving an Extreme Rainfall Detection System with GPM IMERG data

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ITHACA—Information Technology for Humanitarian Assistance, Cooperation and Action, 10138 Torino, Italy
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Politecnico di Torino, Dipartimento di Ingegneria dell’Ambiente, del Territorio e delle Infrastrutture, 10129 Torino, Italy
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Politecnico di Torino, Dipartimento Interateneo di Scienze, Progetto e Politiche del Territorio, 10125 Torino, Italy
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(6), 677; https://doi.org/10.3390/rs11060677
Received: 11 January 2019 / Revised: 16 March 2019 / Accepted: 19 March 2019 / Published: 21 March 2019
(This article belongs to the Section Atmosphere Remote Sensing)
Many studies have shown a growing trend in terms of frequency and severity of extreme events. As never before, having tools capable to monitor the amount of rain that reaches the Earth’s surface has become a key point for the identification of areas potentially affected by floods. In order to guarantee an almost global spatial coverage, NASA Global Precipitation Measurement (GPM) IMERG products proved to be the most appropriate source of information for precipitation retrievement by satellite. This study is aimed at defining the IMERG accuracy in representing extreme rainfall events for varying time aggregation intervals. This is performed by comparing the IMERG data with the rain gauge ones. The outcomes demonstrate that precipitation satellite data guarantee good results when the rainfall aggregation interval is equal to or greater than 12 h. More specifically, a 24-h aggregation interval ensures a probability of detection (defined as the number of hits divided by the total number of observed events) greater than 80%. The outcomes of this analysis supported the development of the updated version of the ITHACA Extreme Rainfall Detection System (ERDS: erds.ithacaweb.org). This system is now able to provide near real-time alerts about extreme rainfall events using a threshold methodology based on the mean annual precipitation. View Full-Text
Keywords: early warning system; extreme events; flood monitoring; GPM; hydrology; rainfall early warning system; extreme events; flood monitoring; GPM; hydrology; rainfall
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Mazzoglio, P.; Laio, F.; Balbo, S.; Boccardo, P.; Disabato, F. Improving an Extreme Rainfall Detection System with GPM IMERG data. Remote Sens. 2019, 11, 677.

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