Assessment of sewer flooding model based on ensemble quantitative precipitation forecast

Lee, Cheng-Shang; Ho, Hsin-Ya; Lee, Kwan Tun; Wang, Yu-Chi; Guo, Wanping; Chen, Delia Yen-Chu; Hsiao, Ling-Feng; Chen, Cheng‐Hsin; Chiang, Chou-Chun; Yang, Ming‐Jen; Kuo, Hung‐Chi

doi:10.1016/j.jhydrol.2012.09.053

Cited by 31 publications

(16 citation statements)

References 21 publications

(17 reference statements)

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“…Fig. In fact, the 2.5-km CReSS is the only cloud-resolving member in TAPEX, where all other members use 5 km in their fine domain (e.g., Lee et al 2013). By using configurations similar to those for research (e.g., Bryan et al 2003;Done et al 2004;Liu et al 2006;Clark et al 2007Clark et al , 2009Roberts and Lean 2008), high-quality operational forecasts out to 3 days as that shown in Fig.…”

Section: Motivation Of Studymentioning

confidence: 99%

The More Rain, the Better the Model Performs—The Dependency of Quantitative Precipitation Forecast Skill on Rainfall Amount for Typhoons in Taiwan

Wang

2015

Monthly Weather Review

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A strong dependency of model performance in quantitative precipitation forecasts (QPFs) as measured by scores such as the threat score (TS) on rainfall amount (i.e., the better the model performs when there is more rain), is demonstrated through real-time forecasts by the 2.5-km Cloud-Resolving Storm Simulator (CReSS) for 15 typhoons in Taiwan in 2010-12. Implied simply from the positive correlation between rain-area sizes and scores, this dependency is expected to exist in all regions, models, and rainfall regimes, while for typhoon QPFs in Taiwan it is also attributed to the model's capability to properly handle (within 72 h) the processes leading to more rain, which are largely controlled by the typhoon's track, size, structure, and environment, and the island's topography. Because of this dependency, the performance of model QPFs for extreme events can be assessed accurately only when forecasts targeted for periods of comparable rainfall magnitude are included for averaging. For the most-rainy 24 h of the top-5 typhoons, the 0-24-h QPFs by CReSS have mean TS of 0.67, 0.67, 0.58, 0.51, and 0.32 at thresholds of 25, 50, 130, 200, and 350 mm, and 0.64, 0.57, 0.37, 0.33, and 0.22 from 48-72-h QPFs, respectively, suggesting superior performance even 2-2.5 days in advance. These scores are strikingly high, precisely because Taiwan can receive extreme rainfall from typhoons. For smaller (nonhazardous) events, the mean scores are progressively lower, but also unimportant and less representative statistically. Therefore, it is inappropriate to average scores over multiple forecasts as those for less-rainy periods would contaminate the result for key periods. The implication for forecasters is also discussed.

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Section: Motivation Of Studymentioning

confidence: 99%

The More Rain, the Better the Model Performs—The Dependency of Quantitative Precipitation Forecast Skill on Rainfall Amount for Typhoons in Taiwan

Wang

2015

Monthly Weather Review

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“…Numerical weather forecasts have been commonly used to provide QPF products in recent years [3][4][5]. However, the global numerical weather forecast models can not meet the requirement of high spatial and temporal resolution of a specific region with complicated topography, due to the limitation of computer resources.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Optimized WRF Precipitation Forecast over a Complex Topography Region during Flood Season

Liu

et al. 2016

Atmosphere

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Abstract:In recent years, the Weather Research and Forecast (WRF) model has been utilized to generate quantitative precipitation forecasts with higher spatial and temporal resolutions. However, factors including horizontal resolution, domain size, and the physical parameterization scheme have a strong impact on the dynamic downscaling ability of the WRF model. In this study, the influence of these factors has been analyzed in precipitation forecasting for the Xijiang Basin, southern China-a region with complex topography. The results indicate that higher horizontal resolutions always result in higher Critical Success Indexes (CSI), but higher biases as well. Meanwhile, the precipitation forecast skills are also influenced by the combination of microphysics parameterization scheme and cumulus convective parameterization scheme. On the basis of these results, an optimized configuration of the WRF model is built in which the horizontal resolution is 10 km, the microphysics parameterization is the Lin scheme, and the cumulus convective parameterization is the Betts-Miller-Janjic scheme. This configuration is then evaluated by simulating the daily weather during the 2013-2014 flood season. The high Critical Success Index scores and low biases at various thresholds and lead times confirm the high accuracy of the optimized WRF model configuration for Xijiang Basin. However, the performance of the WRF model varies from different sub-basins due to the complexity of the mesoscale convective system (MCS) over this region.

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“…Prediction models useful for short-range prediction can differ from the ones that are best for long-term forecasts [24]. It is interesting to notice that simulate flood disaster in cities must also take into account the sewer capacity [25].…”

Section: Floodsmentioning

confidence: 99%

The Internet of Things for Natural Risk Management (Inte.Ri.M.)

Beltramo¹,

Cantore²,

Vesce³

et al. 2019

Perspectives on Risk, Assessment and Management Paradigms

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This chapter deals with the development of a management system, which integrates the use of IoT in natural risk detection, revention, and management with economic evaluation of each stage. In the introductory part, recent data are presented that document the importance that natural disasters have for the environment and for the Italian economy. Section 2 presents the Inte.Ri.M. project-the Internet of Things for Natural Risk Management-its purpose, activity plan, and bodies involved. Technical aspects are treated in Section 3 with the choice of hardware and software components and the solutions for collecting and transmitting data. Section 4 is about the economic aspects considering the stages of prevention, intervention, and restoration and the relation between the intensity of human activity and environment to define a range of situations. These scenarios call for different economic methodologies useful to estimate economic implications of each stage in the short, medium, and long term. Section 5 describes the structure of the Inte.Ri.M. management system and the foreseen functionalities. In the conclusion, the critical points are discussed, and the steps for the transposition of the work carried out on the territory are outlined, according to the provisions of the work program.

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Assessment of sewer flooding model based on ensemble quantitative precipitation forecast

Cited by 31 publications

References 21 publications

The More Rain, the Better the Model Performs—The Dependency of Quantitative Precipitation Forecast Skill on Rainfall Amount for Typhoons in Taiwan

The More Rain, the Better the Model Performs—The Dependency of Quantitative Precipitation Forecast Skill on Rainfall Amount for Typhoons in Taiwan

Evaluation of Optimized WRF Precipitation Forecast over a Complex Topography Region during Flood Season

The Internet of Things for Natural Risk Management (Inte.Ri.M.)

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