Optimal Temporal Resolution of Rainfall for Urban Applications and Uncertainty Propagation

Cecinati, Francesca; Niet, Arie C. de; Sawicka, Kasia; Rico‐Ramirez, Miguel A.

doi:10.3390/w9100762

Cited by 15 publications

(13 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The transition of the Z-R relations from at height to the ground was investigated by Peters et al (2005). Much effort has also been spent on ways to improve QPE by combining radar with other sources of information such as rain gauges (Goudenhoofdt and Delobbe, 2009;Wang et al, 2015) or numerical weather prediction (Bauer et al, 2015) and to quantify the uncertainty of radar-based QPE (Cecinati et al, 2017b).…”

Section: Approaches To Quantitative Precipitation Estimation (Qpe)mentioning

confidence: 99%

Quantitative precipitation estimation with weather radar using a data- and information-based approach

Neuper

Ehret

2019

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. In this study we propose and demonstrate a data-driven approach in an “information-theoretic” framework to quantitatively estimate precipitation. In this context, predictive relations are expressed by empirical discrete probability distributions directly derived from data instead of fitting and applying deterministic functions, as is standard operational practice. Applying a probabilistic relation has the benefit of providing joint statements about rain rate and the related estimation uncertainty. The information-theoretic framework furthermore allows for the integration of any kind of data considered useful and explicitly considers the uncertain nature of quantitative precipitation estimation (QPE). With this framework we investigate the information gains and losses associated with various data and practices typically applied in QPE. To this end, we conduct six experiments using 4 years of data from six laser optical disdrometers, two micro rain radars (MRRs), regular rain gauges, weather radar reflectivity and other operationally available meteorological data from existing stations. Each experiment addresses a typical question related to QPE. First, we measure the information about ground rainfall contained in various operationally available predictors. Here weather radar proves to be the single most important source of information, which can be further improved when distinguishing radar reflectivity–ground rainfall relationships (Z–R relations) by season and prevailing synoptic circulation pattern. Second, we investigate the effect of data sample size on QPE uncertainty using different data-based predictive models. This shows that the combination of reflectivity and month of the year as a two-predictor model is the best trade-off between robustness of the model and information gain. Third, we investigate the information content in spatial position by learning and applying site-specific Z–R relations. The related information gains are only moderate; specifically, they are lower than when distinguishing Z–R relations according to time of the year or synoptic circulation pattern. Fourth, we measure the information loss when fitting and using a deterministic Z–R relation, as is standard practice in operational radar-based QPE applying, e.g., the standard Marshall–Palmer relation, instead of using the empirical relation derived directly from the data. It shows that while the deterministic function captures the overall shape of the empirical relation quite well, it introduces an additional 60 % uncertainty when estimating rain rate. Fifth, we investigate how much information is gained along the radar observation path, starting with reflectivity measured by radar at height, continuing with the reflectivity measured by a MRR along a vertical profile in the atmosphere and ending with the reflectivity observed by a disdrometer directly at the ground. The results reveal that considerable additional information is gained by using observations from lower elevations due to the avoidance of information losses caused by ongoing microphysical precipitation processes from cloud height to ground. This emphasizes both the importance of vertical corrections for accurate QPE and of the required MRR observations. In the sixth experiment we evaluate the information content of radar data only, rain gauge data only and a combination of both as a function of the distance between the target and predictor rain gauge. The results show that station-only QPE outperforms radar-only QPE up to a distance of 7 to 8 km from the nearest station and that radar–gauge QPE performs best, even compared with radar-based models applying season or circulation pattern.

show abstract

Section: Approaches To Quantitative Precipitation Estimation (Qpe)mentioning

confidence: 99%

Quantitative precipitation estimation with weather radar using a data- and information-based approach

Neuper

Ehret

2019

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…Urban hydrological studies need reliable QPEs at high spatial (a few km) and temporal (hourly or sub-hourly) resolutions [53,66]. A few studies have investigated radar-gauge merging techniques focusing on the urban scale but were limited to a single technique under limited climatological and infrastructure conditions [23,[67][68][69]. To date, only two inter-comparison studies have been conducted focusing on urban scales [68,70].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Radar-Gauge Merging Techniques to Be Used in Operational Flood Forecasting in Urban Watersheds

Wijayarathne

Coulibaly

Boodoo

et al. 2020

Water

View full text Add to dashboard Cite

Demand for radar Quantitative Precipitation Estimates (QPEs) as precipitation forcing to hydrological models in operational flood forecasting has increased in the recent past. It is practically impossible to get error-free QPEs due to the intrinsic limitations of weather radar as a precipitation measurement tool. Adjusting radar QPEs with gauge observations by combining their advantages while minimizing their weaknesses increases the accuracy and reliability of radar QPEs. This study deploys several techniques to merge two dual-polarized King City radar (WKR) C-band and two KBUF Next-Generation Radar (NEXRAD) S-band operational radar QPEs with rain gauge data for the Humber River (semi-urban) and Don River (urban) watersheds in Ontario, Canada. The relative performances are assessed against an independent gauge network by comparing hourly rainfall events. The Cumulative Distribution Function Matching (CDFM) method performed best, followed by Kriging with Radar-based Error correction (KRE). Although both WKR and NEXRAD radar QPEs improved significantly, NEXRAD Level III Digital Precipitation Array (DPA) provided the best results. All methods performed better for low- to medium-intensity precipitation but deteriorated with the increasing rainfall intensities. All methods outperformed radar only QPEs for all events, but the agreement is best in the summer.

show abstract

“…In particular, for small urban catchments with drainage area no more than 10 km 2 , the temporal resolution of rainfall (TRR), rather than the spatial resolution of rainfall (SRR), has larger effect on hydrodynamic modelling [5,6]. Data with high TRR contribute to urban hydrology research in many aspects, such as analysing the water balance at short time scale [7], capturing the accurate peak discharge time for flooding events [8], and reducing the uncertainty of hydrological simulation [9].…”

Section: Introductionmentioning

confidence: 99%

Effect of Temporal Resolution of Rainfall on Simulation of Urban Flood Processes

Lyu

Cao

et al. 2018

Water

View full text Add to dashboard Cite

Rainfall exhibits substantial variability, and its temporal resolution considerably affects simulation of hydrological processes. This study aims to investigate the effect of the temporal resolution of rainfall (TRR) on urban flood modeling and to explore how high TRR is required. A routing-enhanced detailed urban stormwater (REDUS) model, which has four layers and accounts for complex urban flow paths, was developed and then applied to the campus of Tsinghua University, Beijing, China. For 30 rainfall events at 1-min resolution, the rainfall accuracy index (RAI) was used to describe the discrepancy of rainfall patterns by upscaling. Through hydrodynamic modelling, the effect of TRR was quantified by the relative error of flood volume and peak in typical areas. The results show that (1) flood peak is sensitive to TRR while flood volume is generally not; (2) with lower TRR, discharge peak is underestimated, and a power function is proposed to express the relationship between the effect of TRR and the characteristics of rainfall and underlying surfaces; and (3) rainfall data of 5-min resolution for urban areas smaller than 1 km 2 , or at least 15-min resolution for larger areas, are required to constrain the relative biases of flood peak within 10%.

show abstract

Optimal Temporal Resolution of Rainfall for Urban Applications and Uncertainty Propagation

Cited by 15 publications

References 42 publications

Quantitative precipitation estimation with weather radar using a data- and information-based approach

Quantitative precipitation estimation with weather radar using a data- and information-based approach

Evaluation of Radar-Gauge Merging Techniques to Be Used in Operational Flood Forecasting in Urban Watersheds

Effect of Temporal Resolution of Rainfall on Simulation of Urban Flood Processes

Contact Info

Product

Resources

About