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Application of probabilistic precipitation forecasts from a deterministic model towards increasing the lead-time of flash flood forecasts in South Africa

机译:从确定性模型中应用概率降水预报,以增加南非山洪预报的提前期

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摘要

Flash floods are some of the most devastating weather-related hazards in South Africa. The South African Flash Flood Guidance (SAFFG) system is a hydro-meteorological modelling system that provides forecasts for the next 1 to 6 h of potential flash floods in support of the flash flood warning system of the South African Weather Service (SAWS). The aim of this paper is to investigate the increase in the lead-time of flash flood warnings of the SAFFG using probabilistic precipitation forecasts generated by the deterministic Unified Model (UM) from the United Kingdom Met Office and run by the South African Weather Service (SAWS). As a first step, calculations of bias-corrected, basin-averaged rainfall from the UM model are provided. An ensemble set of 30 adjacent basins is then identified as ensemble members for each basin (the target basin), from which probabilistic rainfall information is calculated for the target basin covering the extended forecast period. By comparing this probabilistic rainfall forecast with the expected Flash Flood Guidance (FFG) of each basin, an outlook of potential flash flooding is provided. The procedure is applied to a real flash flood event and the ensemble-based rainfall forecasts are verified against rainfall estimated by the SAFFG system. The approach described here is shown to be able to deal with the uncertainties associated with UM rainfall forecasts, particularly regarding location and onset-time of convection. The flash flood outlook for the 18-h extended forecast period investigated was also able to capture the location of the flash flood event and showed its ability to provide additional lead-time for flash flood warnings to disaster managers.
机译:暴洪是南非与天气有关的最具破坏性的灾害。南非山洪指导(SAFFG)系统是一种水文气象建模系统,可为未来1至6小时的潜在山洪提供预报,以支持南非气象局(SAWS)的山洪预警系统。本文的目的是使用由英国气象局确定性统一模型(UM)生成并由南非气象局(UMA)运行的概率降水预报研究SAFFG暴雨洪水预警的前置时间的增加。锯)。第一步,根据UM模型提供经过偏校正的,盆地平均降雨量的计算。然后,将一个由30个相邻流域组成的集合体识别为每个流域(目标流域)的集合体成员,从中计算出覆盖整个预测期的目标流域的概率降雨信息。通过将该概率降雨预报与每个流域的预期山洪预报(FFG)进行比较,可以看到潜在山洪的前景。该程序应用于实际的山洪暴发,并根据SAFFG系统估算的降雨量对基于集合的降雨量预报进行了验证。结果表明,此处描述的方法能够处理与UM雨量预报相关的不确定性,特别是在对流的位置和开始时间方面。在调查的18小时扩展预报期内,山洪暴发的前景也能够捕获山洪暴发事件的位置,并显示出它能够为灾害管理者提供额外的山洪暴发预警准备时间。

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