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A methodology for probabilistic real-time forecasting - an urban case study

机译：概率实时预测的方法-城市案例研究

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摘要

The phenomenon of urban flooding due to rainfall exceeding the design capacity of drainage systems is a global problem and can have significant economic and social consequences. The complex nature of quantitative precipitation forecasts (QPFs) from numerical weather prediction (NWP) models has facilitated a need to model and manage uncertainty. This paper presents a probabilistic approach for modelling uncertainty from single-valued QPFs at different forecast lead times. The uncertainty models in the form of probability distributions of rainfall forecasts combined with a sewer model is an important advancement in real-time forecasting at the urban scale. The methodological approach utilized in this paper involves a retrospective comparison between historical forecasted rainfall from a NWP model and observed rainfall from rain gauges from which conditional probability distributions of rainfall forecasts are derived. Two different sampling methods, respectively, a direct rainfall quantile approach and the Latin hypercube sampling-based method were used to determine the uncertainty in forecasted variables (water level, volume) for a test urban area, the city of Aarhus. The results show the potential for applying probabilistic rainfall forecasts and their subsequent use in urban drainage forecasting for estimation of prediction uncertainty.
机译：由于降雨超过排水系统的设计能力而导致的城市洪水现象是一个全球性问题，可能会产生重大的经济和社会后果。来自数值天气预报（NWP）模型的定量降水预报（QPF）的复杂性质促进了对不确定性进行建模和管理的需求。本文提出了一种概率方法，用于对不同预测提前期下单值QPF的不确定性进行建模。降雨预报的概率分布形式的不确定性模型与下水道模型相结合，是城市规模实时预报的重要进步。本文采用的方法学方法是对NWP模型的历史预报降雨量与雨量计的观测降雨量进行回顾性比较，从而得出降雨预报的条件概率分布。使用两种不同的采样方法（分别是直接降雨分位数方法和基于拉丁超立方采样的方法）来确定测试市区（奥胡斯）的预测变量（水位，水量）的不确定性。结果表明，可能应用概率降雨预报及其随后在城市排水预报中用于估算预报不确定性的潜力。

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