Empirically based modelling of radar‐rainfall uncertainties for a C‐band radar at different time‐scales

Villarini, Gabriele; Krajewski, Witold F.

doi:10.1002/qj.454

Cited by 35 publications

(30 citation statements)

References 37 publications

(80 reference statements)

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“…Following our methodology, we forced our model with the radar‐rainfall input generated by a recently developed error model of the NEXRAD‐based hourly rainfall maps [ Ciach et al , 2007; Villarini and Krajewski , 2009]. The model is based on a large sample of empirical data and has a flexible structure, where the total error in radar‐rainfall estimate is separated into systematic and random errors.…”

Section: Methodsmentioning

confidence: 99%

“…Uncertainties in radar rainfall estimates have been studied for more than 30 years [ Grayman and Eagleson , 1971; Wilson and Brandes , 1979; Cluckie and Collier , 1991; Ciach and Krajewski , 1999a, 1999b; Seed et al , 1999; Pegram and Clothier , 2001; Jordan et al , 2003; Chumchean et al , 2006; Ciach et al , 2007; Habib et al , 2008; Krajewski et al , 2010; AghaKouchak et al , 2010a, 2010b], and several models have been proposed for the statistical description of radar‐rainfall errors (see reviews by Villarini and Krajewski , 2009; Mandapaka and Germann , 2010]). Early methods of simulating synthetic radar‐rainfall fields, i.e., rainfall fields that are corrupted by radar‐like systematic and random errors, were based on a conceptual understanding of the uncertainties involved [ Krajewski and Georgakakos , 1985; Krajewski , 1993; Anagnostou and Krajewski , 1997; Carpenter and Georgakakos , 2004].…”

Section: Introductionmentioning

confidence: 99%

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Impact of radar‐rainfall error structure on estimated flood magnitude across scales: An investigation based on a parsimonious distributed hydrological model

Cunha

Mandapaka

Krajewski

et al. 2012

Water Resources Research

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[1] The goal of this study is to diagnose the manner in which radar-rainfall input affects peak flow simulation uncertainties across scales. We used the distributed physically based hydrological model CUENCAS with parameters that are estimated from available data and without fitting the model output to discharge observations. We evaluated the model's performance using (1) observed streamflow at the outlet of nested basins ranging in scale from 20 to 16,000 km 2 and (2) streamflow simulated by a well-established and extensively calibrated hydrological model used by the US National Weather Service (SAC-SMA). To mimic radar-rainfall uncertainty, we applied a recently proposed statistical model of radar-rainfall error to produce rainfall ensembles based on different expected error scenarios. We used the generated ensembles as input for the hydrological model and summarized the effects on flow sensitivities using a relative measure of the ensemble peak flow dispersion for every link in the river network. Results show that peak flow simulation uncertainty is strongly dependent on the catchment scale. Uncertainty decreases with increasing catchment drainage area due to the aggregation effect of the river network that filters out small-scale uncertainties. The rate at which uncertainty changes depends on the error structure of the input rainfall fields. We found that random errors that are uncorrelated in space produce high peak flow variability for small scale basins, but uncertainties decrease rapidly as scale increases. In contrast, spatially correlated errors produce less scatter in peak flows for small scales, but uncertainty decreases slowly with increasing catchment size. This study demonstrates the large impact of scale on uncertainty in hydrological simulations and demonstrates the need for a more robust characterization of the uncertainty structure in radar-rainfall. Our results are diagnostic and illustrate the benefits of using the calibrationfree, multiscale framework to investigate uncertainty propagation with hydrological models.Citation: Cunha, L. K., P. V. Mandapaka, W. F. Krajewski, R. Mantilla, and A. A. Bradley (2012), Impact of radar-rainfall error structure on estimated flood magnitude across scales: An investigation based on a parsimonious distributed hydrological model, Water Resour. Res., 48, W10515,

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Impact of radar‐rainfall error structure on estimated flood magnitude across scales: An investigation based on a parsimonious distributed hydrological model

Cunha

Mandapaka

Krajewski

et al. 2012

Water Resources Research

Self Cite

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“…Currently, there is a significant amount of work in quantifying the errors in RR (Ciach et al, 2007;Villarini and Krajewski, 2009;Germann et al, 2009;Quintero et al, 2012;Dai et al, 2014a;Dai et al, 2014b). The knowledge of the uncertainties affecting RR measurements can be effectively used to build a hydro-meteorological forecasting system in a probabilistic framework.…”

Section: Introductionmentioning

confidence: 99%

Quantifying radar-rainfall uncertainties in urban drainage flow modelling

Rico‐Ramirez

Liguori

Schellart

2015

Journal of Hydrology

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a b s t r a c tThis work presents the results of the implementation of a probabilistic system to model the uncertainty associated to radar rainfall (RR) estimates and the way this uncertainty propagates through the sewer system of an urban area located in the North of England. The spatial and temporal correlations of the RR errors as well as the error covariance matrix were computed to build a RR error model able to generate RR ensembles that reproduce the uncertainty associated with the measured rainfall. The results showed that the RR ensembles provide important information about the uncertainty in the rainfall measurement that can be propagated in the urban sewer system. The results showed that the measured flow peaks and flow volumes are often bounded within the uncertainty area produced by the RR ensembles. In 55% of the simulated events, the uncertainties in RR measurements can explain the uncertainties observed in the simulated flow volumes. However, there are also some events where the RR uncertainty cannot explain the whole uncertainty observed in the simulated flow volumes indicating that there are additional sources of uncertainty that must be considered such as the uncertainty in the urban drainage model structure, the uncertainty in the urban drainage model calibrated parameters, and the uncertainty in the measured sewer flows.

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“…Because of these disadvantages, more and more efforts have been focused on the empirical error model (Habib et al ., ; Villarini and Krajewski, ; Bringi et al ., ; Habib and Qin, ). In recent studies, some strict statistical models have been proposed (Ciach et al ., ; Germann et al ., ; Aghakouchak et al ., ).…”

Section: Discussionmentioning

confidence: 99%

Radar rainfall uncertainty modelling influenced by wind

Dai

Han²,

Rico‐Ramirez

et al. 2014

Hydrological Processes

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Abstract:Radar-based estimates of rainfall are affected by many sources of uncertainties, which would propagate through the hydrological model when radar rainfall estimates are used as input or initial conditions. An elegant solution to quantify these uncertainties is to model the empirical relationship between radar measurements and rain gauge observations (as the 'ground reference'). However, most current studies only use a fixed and uniform model to represent the uncertainty of radar rainfall, without consideration of its variation under different synoptic regimes. Wind is such a typical weather factor, as it not only induces error in rain gauge measurements but also causes the raindrops observed by weather radar to drift when they reach the ground. For this reason, as a first attempt, this study introduces the wind field into the uncertainty model and designs the radar rainfall uncertainty model under different wind conditions. We separate the original dataset into three subsamples according to wind speed, which are named as WDI (0-2 m/s), WDII (2-4 m/s) and WDIII (>4 m/s). The multivariate distributed ensemble generator is introduced and established for each subsample. Thirty typical events (10 at each wind range) are selected to explore the behaviours of uncertainty under different wind ranges. In each time step, 500 ensemble members are generated, and the values of 5th to 95th percentile values are used to produce the uncertainty bands. Two basic features of uncertainty bands, namely dispersion and ensemble bias, increase significantly with the growth of wind speed, demonstrating that wind speed plays a considerable role in influencing the behaviour of the uncertainty band. On the basis of these pieces of evidence, we conclude that the radar rainfall uncertainty model established under different wind conditions should be more realistic in representing the radar rainfall uncertainty. This study is only a start in incorporating synoptic regimes into rainfall uncertainty analysis, and a great deal of more effort is still needed to build a realistic and comprehensive uncertainty model for radar rainfall data.

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Empirically based modelling of radar‐rainfall uncertainties for a C‐band radar at different time‐scales

Cited by 35 publications

References 37 publications

Impact of radar‐rainfall error structure on estimated flood magnitude across scales: An investigation based on a parsimonious distributed hydrological model

Impact of radar‐rainfall error structure on estimated flood magnitude across scales: An investigation based on a parsimonious distributed hydrological model

Quantifying radar-rainfall uncertainties in urban drainage flow modelling

Radar rainfall uncertainty modelling influenced by wind

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