A methodology for probabilistic real-time forecasting – an urban case study
Journal of Hydroinformatics
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.
Copyright © IWA Publishing 2013. The definitive peer-reviewed and edited version of this article is published in Journal of Hydroinformatics, Volume 15 (3), pp. 751-762 (2013), DOI:10.2166/hydro.2012.031 and is available at www.iwapublishing.com
Vol. 15 (3), pp. 751-762
Showing items related by title, author, creator and subject.
Siegert, S; Sansom, PG; Williams, R (Wiley / Royal Meteorological Society, 2016-02-09)Ensemble forecasts of weather and climate are subject to systematic biases in the ensemble mean and variance, leading to inaccurate estimates of the forecast mean and variance. To address these biases, ensemble forecasts ...
Mitchell, K; Ferro, CAT (Wiley / Royal Meteorological Society, 2017-05-18)Interval probabilistic forecasts for a binary event are forecasts issued as a range of probabilities for the occurrence of the event, for example, ‘chance of rain: 10-20%’. To verify interval probabilistic forecasts, use ...
Fieldsend, Jonathan E.; Singh, Sameer (Institute of Electrical and Electronics Engineers (IEEE), 2002)Recent studies confront the problem of multiple error terms through summation. However this implicitly assumes prior knowledge of the problem's error surface. This study constructs a population of Pareto optimal Neural ...