Effects of Using High-Density Rain Gauge Networks and Weather Radar Data on Urban Hydrological Analyses

Yoon, Sugjoon; Lee, Byongju

doi:10.3390/w9120931

Cited by 18 publications

(18 citation statements)

References 27 publications

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“…On a daily scale, Burcea et al (2012) [35] also found that meteorological radar in the Moldavian Plaeau underestimated rainfall recorded by rain gauges. Changing the time scale from daily to ten minutes, similar results have also been documented in Seoul (South Corea) [39].…”

Section: Discussionsupporting

confidence: 76%

Assessment of Satellite and Radar Quantitative Precipitation Estimates for Real Time Monitoring of Meteorological Extremes Over the Southeast of the Iberian Peninsula

2018

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Quantitative Precipitation Estimates (QPEs) obtained from remote sensing or ground-based radars could complement or even be an alternative to rain gauge readings. However, to be used in operational applications, a validation process has to be carried out, usually by comparing their estimates with those of a rain gauges network. In this paper, the accuracy of three QPEs are evaluated for three extreme precipitation events in the last decade in the southeast of the Iberian Peninsula. The first QPE is PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks -Cloud Classification System) , a satellite-based QPE. The second and the third are QPEs from a meteorological radar with Doppler capabilities that works in the C band. Pixel-to-point comparisons are made between the values offered by the QPEs and those obtained by two networks of rain gauges. The results obtained indicate that all the QPEs were well below the rain gauge values in extreme rainfall time slots. There seems to be a weak linear association between the value of the discrepancies and the precipitation value of the QPEs. The main conclusion, assuming the information from the rain gauges as ground truth, is that neither PERSIANN-CCS nor radar, without empirical calibration, are acceptable QPEs for the real-time monitoring of meteorological extremes in the southeast of the Iberian Peninsula.

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Section: Discussionsupporting

confidence: 76%

Assessment of Satellite and Radar Quantitative Precipitation Estimates for Real Time Monitoring of Meteorological Extremes Over the Southeast of the Iberian Peninsula

2018

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“…Seoul is one of the most highly urbanized cities in South Korea with several residential areas and industrial facilities located near flood plains that are at risk from damage caused by urban flooding. Several researchers, and even the South Korean government, have worked to develop flood forecasting systems and methodologies for urban flood disaster mitigation [22][23][24]. Moreover, many kinds of QPF data are issued for the Seoul metropolitan area by the KMA, allowing us to easily evaluate the hydrological applicability and accuracy of various QPFs.…”

Section: Study Areamentioning

confidence: 99%

Adaptive Blending Method of Radar-Based and Numerical Weather Prediction QPFs for Urban Flood Forecasting

Yoon

2019

Remote Sensing

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Preparing proper disaster prevention measures is important for decreasing the casualties and property losses resulting from floods. One of the most efficient measures in this regard is real-time flood forecasting using quantitative precipitation forecasts (QPFs) based on either short-term radar-based extrapolation or longer-term numerical weather prediction. As both methods have individual advantages and limitations, in this study we developed a new real-time blending technique to improve the accuracy of rainfall forecasts for hydrological applications. We tested the hydrological applicability of six QPFs used for urban flood forecasting in Seoul, South Korea: the McGill Algorithm for Prediction Nowcasting by Lagrangian Extrapolation (MAPLE), KOrea NOwcasting System (KONOS), Spatial-scale Decomposition method (SCDM), Unified Model Local Data Assimilation and Prediction System (UM LDAPS), and Advanced Storm-scale Analysis and Prediction System (ASAPS), as well as our proposed blended approach based on the assumption that the error of the previously predicted rainfall is similar to that of current predicted rainfall. We used the harmony search algorithm to optimize real-time weights that would minimize the root mean square error between predicted and observed rainfall for a 1 h lead time at 10 min intervals. We tested these models using the Storm Water Management Model (SWMM) and Grid-based Inundation Analysis Model (GIAM) to estimate urban flood discharge and inundation using rainfall from the QPFs as input. Although the blended QPF did not always have the highest correlation coefficient, its accuracy varied less than that of the other QPFs. In addition, its simulated water depth in pipe and spatial extent were most similar to observed inundated areas, demonstrating the value of this new approach for short-term flood forecasting.

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“…Rain gauges provide close to ground-truth precipitation observations, but very few networks with >50 gauges, densities of >0.1 gauges/1 km 2 , and subhourly recordings required to study convective area precipitation exist around the globe (e.g., Moore et al, 2000;Singer & Michaelides, 2017;Yoon & Lee, 2017). The very high density, long-term climate station network WegenerNet Feldbach Region (WEGN) in southeastern Austria provides a unique setting with 150 gauges (density 0.5 gauges/1 km 2 ).…”

Section: Introductionmentioning

confidence: 99%

Strong Dependence of Extreme Convective Precipitation Intensities on Gauge Network Density

Schroeer

Kirchengast

Sungmin

2018

Geophysical Research Letters

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Extreme convective precipitation on subhourly scales is notoriously misrepresented in rain gauge‐based observations, but uncertainties are weakly quantified at the 1 to 30 km scale. We employ a unique observing network, the high‐density WegenerNet and surrounding operational rain gauge network in southeastern Austria, to sample convective precipitation extremes at these scales. By systematically constructing lower‐density networks, we explore how estimated maximum area precipitation depends on observing station density. Using subhourly to hourly temporal resolution, we find a d−0.5(±0.1) power law decay of the event maximum area precipitation over distances d from 1 to 30 km, showing that operational gauge networks underrate extreme convective precipitation falling over small areas. Furthermore, extremes at point scale are found underestimated by operational networks by about 20%. We consider the dependencies representative for short‐duration convective events over similar regions at midlatitudes and the results valuable for high‐resolution climate model evaluation.

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Effects of Using High-Density Rain Gauge Networks and Weather Radar Data on Urban Hydrological Analyses

Cited by 18 publications

References 27 publications

Assessment of Satellite and Radar Quantitative Precipitation Estimates for Real Time Monitoring of Meteorological Extremes Over the Southeast of the Iberian Peninsula

Assessment of Satellite and Radar Quantitative Precipitation Estimates for Real Time Monitoring of Meteorological Extremes Over the Southeast of the Iberian Peninsula

Adaptive Blending Method of Radar-Based and Numerical Weather Prediction QPFs for Urban Flood Forecasting

Strong Dependence of Extreme Convective Precipitation Intensities on Gauge Network Density

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