A percentile‐based approach to rainfall scenario construction for surface‐water flood forecasts,Meteorological Applications

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A percentile‐based approach to rainfall scenario construction for surface‐water flood forecasts
Meteorological Applications ( IF 2.3 ) Pub Date : 2020-11-22 , DOI: 10.1002/met.1963
Steven J. Böing ₁ , Cathryn E. Birch ₁ , Benjamin L. Rabb ₁ , Kay L. Shelton ₂

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A novel technique to produce reasonable worst‐case rainfall scenarios from ensemble forecasts is presented. This type of scenario is relevant for predicting the risk of localized, intense rainfall events with a duration between 15 min and several hours. Such rainfall events can cause surface‐water (pluvial) flooding. Producing useful forecasts of these events at lead times of more than a few hours is challenging due to the precision and accuracy in rainfall intensity, duration and location that is required. The technique described here addresses these challenges by constructing appropriate scenarios using a neighbourhood technique in combination with ensemble forecasting. It is similar to the distance‐dependent depth–duration analysis described in earlier studies, but it introduces an additional post‐processing step based on probability distribution functions of rainfall accumulation near a location of interest. This additional step makes the reasonable worst‐case scenarios less dependent on grid‐scale behaviour, and helps to generate scenarios with a consistent interpretation. The method is used to compare forecasts with a lead time of 6–36 hr to radar data for several case studies that occurred in Yorkshire. These comparisons also introduce new techniques to present maps of the reasonable worst‐case rainfall accumulation at each location.

中文翻译：

基于百分位数的地表洪水预报降雨方案构建方法

提出了一种从集合预报中产生合理的最坏情况降雨情景的新技术。这种情况与预测持续15分钟到几个小时的局部强降雨事件的风险有关。此类降雨事件可能导致地表水（河流）洪水泛滥。由于所需的降雨强度，持续时间和位置的精确性和准确性，要在提前几个小时以上的时间为这些事件产生有用的预测是具有挑战性的。此处描述的技术通过结合邻域技术和集成预测来构建适当的方案来解决这些挑战。它类似于早期研究中描述的基于距离的深度持续时间分析，但是它引入了一个额外的后处理步骤，该步骤基于目标位置附近降雨累积的概率分布函数。此附加步骤使合理的最坏情况场景较少依赖于网格规模的行为，并有助于生成具有一致解释的场景。对于在约克郡发生的一些案例研究，该方法用于将提前期为6–36小时的预测与雷达数据进行比较。这些比较还引入了新技术，以显示每个位置合理的最坏情况下的降雨累积图。对于约克郡发生的一些案例研究，该方法用于将提前期为6–36小时的预测与雷达数据进行比较。这些比较还引入了新技术，以显示每个位置合理的最坏情况下的降雨累积图。对于在约克郡发生的一些案例研究，该方法用于将提前期为6–36小时的预测与雷达数据进行比较。这些比较还引入了新技术，以显示每个位置合理的最坏情况下的降雨累积图。

更新日期：2020-11-23

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