Библиотека на Университет по архитектура, строителство и геодезия

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ERDS: an Extreme Rainfall Detection System Based on Both near Real-Time and Forecast Rainfall Measurements

Осми българо-австрийски семинар "Хидроложки опасности и свързаните с тях проблеми", 30 – 31 май 2019
P. Mazzoglio; S. Balbo; P. Boccardo; F. Laio
ERDS: an Extreme Rainfall Detection System Based on Both near Real-Time and Forecast Rainfall Measurements
ЦБ II 544

Детайли

Източник
Годишник на УАСГ
Година на издаване
2019
Пор.№
Бр.S1
Страници
с. S1423-1433
Том
52
Ключови думи
early warning system, extreme events, flood monitoring, GPM, rainfall
Отрасъл
Общ отдел. Наука. Библ. дело. Енциклопедии
Анотация
Extreme rainfall may trigger some of the most catastrophic natural disasters, whose consequences may be exacerbated especially in places where an appropriate network of measurement instruments is not available. A combination of remotely sensed data and weather prediction model outputs can often help to obtain information with a global spatial coverage without the limitations that characterize other instruments. In order to achieve this goal, an Extreme Rainfall Detection System (ERDS - erds.ithacaweb.org) was developed and implemented with the aim of monitoring and forecasting exceptional rainfall events. The system was designed with the aim of providing information in an understandable way also for non-specialized users. The NOAA-GFS deterministic weather prediction model is used for the purpose of forecasting extreme rainfall events. Regarding the near real-time rainfall monitoring, the previous version of ERDS was using NASA TRMM TMPA 3-hourly data as input. Due to TRMM instrument shutdown, a different rainfall measurement must be used. NASA GPM IMERG early run half-hourly data proved to be the proper one. A comparison between GPM and rain gauge data allowed to define the minimum time aggregation intervals to be used for the detection of extreme rainfall events in order to reduce the effects of the bias due to satellite data. The same comparison was also performed using GFS data instead of GPM data.
Системен №
4928

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