Propagation of uncertainty from observing systems and NWP into hydrological models: COST‐731 Working Group 2

Zappa, Massimiliano; Beven, Keith; Bruen, Michael; Cofiño, Antonio S.; Kok, Kees; Martin, E.; Nurmi, Pertti; Orfila, B.; Roulin, Emmanuel; Schröter, Kai; Seed, Alan; Szturc, Jan; Vehviläinen, Bertel; Germann, Urs; Rossa, Andrea

doi:10.1002/asl.248

Cited by 91 publications

(64 citation statements)

References 61 publications

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“…It is important to accept that certain errors are unavoidable and to understand how uncertainty cascades through hydrological modelling systems. This is a key aspect of the Cooperation in Science and Technology Action 731 (COST-731; see Rossa et al, 2010;Zappa et al, 2010).…”

Section: Discussionmentioning

confidence: 99%

Streamflow Modelling: A Primer on Applications, Approaches and Challenges

2012

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This article examines the current practice of streamflow modelling, a field under development for over a century. A sample of the wide range of assessment and planning applications of streamflow models is presented. The diversity in the use of these models is mirrored in the diversity of model complexity, and modelling approaches ranging from empirical to physically based and from lumped to fully distributed are described with examples. Predictions derived from hydrological models are subject to many sources of error; these are discussed along with methods for error minimization or anticipation. Model error is generally quantified using an ensemble of forecasts meant to sample the range of predictive uncertainty. This ensemble can be used to generate reliable probabilistic forecasts of hydrological quantities if all sources of error are accounted for. To date, applications of ensemble methods in streamflow forecasting have typically focused on only one or two error sources. A challenge will be to develop ensemble streamflow forecasts that sample a wider range of predictive uncertainty.

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Section: Discussionmentioning

confidence: 99%

Streamflow Modelling: A Primer on Applications, Approaches and Challenges

2012

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“…Many papers are especially addressing quality related issues in weather radar measurements, for instance, a comprehensive review presented by Michelson et al (2005) is available. Moreover, in the frame of COST Actions, especially 717 (Šálek et al, 2004;Michelson et al, 2005) and 731 (Zappa et al, 2010), have focused on radar data quality and resulting uncertainty in the data.…”

Section: Previous Workmentioning

confidence: 99%

The quality index for radar precipitation data: a tower of Babel?

Einfalt

Szturc

Ośródka

2010

Atmospheric Science Letters

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One of the quantitative metrics of quality of radar measurements of precipitation is the quality index (QI): a field of numbers whose values depend on the quality. Such an approach is operationally used in some national meteorological services. Difficulties in using this approach can be observed due to hardware and software differences and continuous quality control algorithm improvement. An overall review of commonly used approaches and connected difficulties is made. The challenges in hydrological applications using the QI are listed, as the technique is used to generate precipitation field ensembles. Recommendations for future common considerations are suggested.

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“…Despite great efforts, the measurement and prediction of rainfall at fine space-time resolutions is still associated with large errors. Consequently, it is of crucial importance to accurately characterise the uncertainty related to its measurement and prediction, in particular for hydrometeorological applications that are sensitive to input precipitation uncertainty (Zappa et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

A non-stationary stochastic ensemble generator for radar rainfall fields based on the short-space Fourier transform

Nerini

Bešič

Sideris

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. In this paper we present a non-stationary stochastic generator for radar rainfall fields based on the short-space Fourier transform (SSFT). The statistical properties of rainfall fields often exhibit significant spatial heterogeneity due to variability in the involved physical processes and influence of orographic forcing. The traditional approach to simulate stochastic rainfall fields based on the Fourier filtering of white noise is only able to reproduce the global power spectrum and spatial autocorrelation of the precipitation fields. Conceptually similar to wavelet analysis, the SSFT is a simple and effective extension of the Fourier transform developed for space-frequency localisation, which allows for using windows to better capture the local statistical structure of rainfall. The SSFT is used to generate stochastic noise and precipitation fields that replicate the local spatial correlation structure, i.e. anisotropy and correlation range, of the observed radar rainfall fields. The potential of the stochastic generator is demonstrated using four precipitation cases observed by the fourth generation of Swiss weather radars that display significant non-stationarity due to the coexistence of stratiform and convective precipitation, differential rotation of the weather system and locally varying anisotropy. The generator is verified in its ability to reproduce both the global and the local Fourier power spectra of the precipitation field. The SSFT-based stochastic generator can be applied and extended to improve the probabilistic nowcasting of precipitation, design storm simulation, stochastic numerical weather prediction (NWP) downscaling, and also for other geophysical applications involving the simulation of complex nonstationary fields.

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Propagation of uncertainty from observing systems and NWP into hydrological models: COST‐731 Working Group 2

Cited by 91 publications

References 61 publications

Streamflow Modelling: A Primer on Applications, Approaches and Challenges

Streamflow Modelling: A Primer on Applications, Approaches and Challenges

The quality index for radar precipitation data: a tower of Babel?

A non-stationary stochastic ensemble generator for radar rainfall fields based on the short-space Fourier transform

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