A Stochastic Model of Radar Measurement Errors in Rainfall Accumulations at Catchment Scale

Jordan, Phillip; Seed, Alan; Weinmann, Peter Erwin

doi:10.1175/1525-7541(2003)004<0841:asmorm>2.0.co;2

Cited by 45 publications

(44 citation statements)

References 38 publications

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“…The analog-based approach derives the forecast probability density function (pdf) by retrieving a set of similar situations from an archive of precipitation events (Panziera et al, 2011;, the local Lagrangian approach derives the pdf by collecting the precipitation values in a neighborhood of a given grid point in Lagrangian frame of reference (Hohti et al, 2000;Germann and Zawadzki, 2004) and the stochastic approach exploits a random number generator to compute an ensemble of equally likely precipitation fields, for example by adding stochastic perturbations to a deterministic extrapolation nowcast (Pegram and Clothier, 2001a, b;Bowler et al, 2006;Metta et al, 2009;Berenguer et al, 2011;Seed et al, 2013;Atencia and Zawadzki, 2014;Dai et al, 2015). The stochastic approach is also extensively used to produce ensembles of precipitation fields that characterize the radar measurement uncertainty (e.g., Jordan et al, 2003;Germann et al, 2009) and for design storm studies (e.g., Willems, 2001a;Paschalis et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Development and verification of a real-time stochastic precipitation nowcasting system for urban hydrology in Belgium

Foresti

Reyniers

Seed

et al. 2016

Hydrol. Earth Syst. Sci.

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Abstract. The Short-Term Ensemble Prediction System (STEPS) is implemented in real-time at the Royal Meteorological Institute (RMI) of Belgium. The main idea behind STEPS is to quantify the forecast uncertainty by adding stochastic perturbations to the deterministic Lagrangian extrapolation of radar images. The stochastic perturbations are designed to account for the unpredictable precipitation growth and decay processes and to reproduce the dynamic scaling of precipitation fields, i.e., the observation that largescale rainfall structures are more persistent and predictable than small-scale convective cells. This paper presents the development, adaptation and verification of the STEPS system for Belgium (STEPS-BE). STEPS-BE provides in realtime 20-member ensemble precipitation nowcasts at 1 km and 5 min resolutions up to 2 h lead time using a 4 C-band radar composite as input. In the context of the PLURISK project, STEPS forecasts were generated to be used as input in sewer system hydraulic models for nowcasting urban inundations in the cities of Ghent and Leuven. Comprehensive forecast verification was performed in order to detect systematic biases over the given urban areas and to analyze the reliability of probabilistic forecasts for a set of case studies in 2013 and 2014. The forecast biases over the cities of Leuven and Ghent were found to be small, which is encouraging for future integration of STEPS nowcasts into the hydraulic models. Probabilistic forecasts of exceeding 0.5 mm h −1 are reliable up to 60-90 min lead time, while the ones of exceeding 5.0 mm h −1 are only reliable up to 30 min. The STEPS ensembles are slightly under-dispersive and represent only 75-90 % of the forecast errors.

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Section: Introductionmentioning

confidence: 99%

Development and verification of a real-time stochastic precipitation nowcasting system for urban hydrology in Belgium

Foresti

Reyniers

Seed

et al. 2016

Hydrol. Earth Syst. Sci.

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“…In pursuit of more realistic synthetic radar rainfall data, models evolved from being purely statistical, or on the basis of a conceptual understating of the uncertainty involved [e.g., Krajewski and Georgakakos, 1985;Carpenter et al, 2001;Georgakakos and Carpenter, 2003;Russo et al, 2006], to a hybrid approach that combined statistical descriptions of space-time rainfall with a physicsbased mechanism of radar observations [e.g., Krajewski et al, 1993Krajewski et al, , 1996Anagnostou and Krajewski, 1997;Borga et al, 1997;Jordan et al, 2003]. Further realism was attained by replacing statistical models of rainfall with high-resolution mesoscale numerical weather prediction models [e.g., Sharif et al, 2002Sharif et al, , 2004.…”

Section: Introductionmentioning

confidence: 99%

Product‐error‐driven generator of probable rainfall conditioned on WSR‐88D precipitation estimates

Villarini

Krajewski

Ciach

et al. 2009

Water Resources Research

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[1] The existence of large errors in precipitation products delivered by the network of Weather Surveillance Radar, 1988 Doppler (WSR-88D) radars is broadly recognized. However, their quantitative characteristics remain poorly understood. Recently, the authors developed a functional-statistical model that quantifies the relation between radar rainfall and the corresponding true rainfall in a way that is applicable to the probabilistic quantitative precipitation estimation planned for future use by the U.S. National Weather Service. The model consists of a deterministic distortion function and a random uncertainty factor, both conditioned on given radar rainfall values. It also accounts for the spatiotemporal correlations in the random uncertainty factor. The model components were estimated on the basis of a 6-year-long data sample that considers the effects of seasons, range from radar, and time scales. In this study, the authors present two different applications of the aforementioned uncertainty model: (1) the estimation of rainfall probability maps and (2) the generation of radar rainfall ensembles. In the former, maps of the rainfall exceedance probability for any threshold are produced, given a radar rainfall map. We also present the analytical derivation of the exceedance probability maps at coarser spatial scales. In the latter, the users can generate ensembles of probable true rainfall fields that are consistent with the observed radar rainfall and its error structure. Simulation of the random component is based on the Cholesky decomposition method. Finally, the authors discuss possible uses of these applications in hydrology and hydroclimatology.

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“…The need to account for error dependency was recently investigated (Nijssen & Lettenmaier, 2004;Hossain & Anagnostou, 2006) for satellite precipitation estimates, which have error properties that are quite different from those of the radar estimates. Jordan et al (2003) developed and calibrated a random cascade model of radar errors that accounts for error correlations. Their results indicated non-negligible levels of error correlations especially in space.…”

Section: Introductionmentioning

confidence: 99%

Analysis of radar-rainfall error characteristics and implications for streamflow simulation uncertainty

Habib

Aduvala

Meselhe

2008

Hydrological Sciences Journal

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A Stochastic Model of Radar Measurement Errors in Rainfall Accumulations at Catchment Scale

Cited by 45 publications

References 38 publications

Development and verification of a real-time stochastic precipitation nowcasting system for urban hydrology in Belgium

Development and verification of a real-time stochastic precipitation nowcasting system for urban hydrology in Belgium

Product‐error‐driven generator of probable rainfall conditioned on WSR‐88D precipitation estimates

Analysis of radar-rainfall error characteristics and implications for streamflow simulation uncertainty

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