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

Villarini, Gabriele; Krajewski, Witold F.; Ciach, Grzegorz J.; Zimmerman, Dale L.

doi:10.1029/2008wr006946

Cited by 72 publications

(79 citation statements)

References 91 publications

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“…Cain and Smith, 1976). At the present stage the use of the Gaussian distribution is instrumental for the generation of ensembles of rainfall fields as proposed and developed by Villarini et al (2009). Using the parameters found in this study and conditioning the ensemble on the products from the Wardon Hill radar one could study error propagation for different applications in the region.…”

Section: Discussionmentioning

confidence: 99%

“…A comparison of the results for different network set-ups reveals that set-up III's Gaussian quantiles better approximate the empirical ones at shorter time-scales, while at larger time-scales they all seem to perform reasonably well. As discussed by Villarini et al (2009), the fact that the random component can be described by a Gaussian distribution is instrumental in the development of a generator of probable true rainfall fields conditioned on given radarrainfall maps.…”

Section: Random Componentmentioning

confidence: 99%

“…These elements are important for a realistic simulation of radar-rainfall uncertainties in all of those studies interested in the propagation of these errors through models in which radar-rainfall is used as input (see Villarini et al (2009) for more details). At present, we have very limited information about their space-time correlation (e.g.…”

Section: Random Componentmentioning

confidence: 99%

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

Villarini

Krajewski

2009

Quart J Royal Meteoro Soc

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Radar-based estimates of rainfall are affected by several sources of systematic and random errors. The propagation of these uncertainties is required for improving the understanding and interpretation of the results obtained when radar-rainfall estimates are used as input or initial conditions. Despite the relevance of the problem, modelling of radar-rainfall uncertainties is still at an early stage. The authors apply an empirically based model in which the relation between true rainfall and radar-rainfall can be described as the product of a systematic distortion function (accounting for systematic biases) and random component (describing the remaining random errors). The proposed results are based on a large sample (more than six years) of data from a C-band radar (Wardon Hill) located in southwest England. The true ground rainfall is approximated by rain gauge measurements from a highly dense network deployed in the Brue catchment, located about 40 km from the radar site.The authors present results from their modelling of the radar-rainfall errors at four time-scales of hydrologic interest (5-and 15-minute, hourly and three-hourly) and 2 km spatial resolution, discussing the impact of spatial sampling errors on the proposed results.

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Section: Discussionmentioning

confidence: 99%

Section: Random Componentmentioning

confidence: 99%

Section: Random Componentmentioning

confidence: 99%

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

Villarini

Krajewski

2009

Quart J Royal Meteoro Soc

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“…The residual radar measurement uncertainty can be effectively modelled by the non-stationary generator to obtain QPE ensembles, which reproduce the local statistical characteristics and anisotropy of the observed rainfall fields (Jordan et al, 2003;Ciach et al, 2007;Villarini et al, 2009;Germann et al, 2009;Cecinati et al, 2017). In addition, we believe that current radar rain gauge merging and adjustment techniques (e.g.…”

Section: Future Perspectivesmentioning

confidence: 99%

“…An important group of techniques generates the stochastic perturbations by LU or Cholesky decomposition of the covariance matrix, which represents the multiplicative radar and rain gauge errors (see, e.g., Germann et al, 2009;Villarini et al, 2009). Given the direct link between the covariance and the Fourier spectrum via the Wiener-Khintchine theorem, it is not a surprise to observe that FFT-based noise generators are also used for ensemble radar-based QPE (Jordan et al, 2003;Pegram et al, 2011).…”

Section: Brief Review Of Spatial Stochastic Rainfall Generatorsmentioning

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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Probabilistic precipitation rate estimates with ground‐based radar networks

Kirstetter

Gourley

Hong

et al. 2015

Water Resources Research

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The uncertainty structure of radar quantitative precipitation estimation (QPE) is largely unknown at fine spatiotemporal scales near the radar measurement scale. By using the WSR-88D radar network and gauge data sets across the conterminous US, an investigation of this subject has been carried out within the framework of the NOAA/NSSL ground radar-based Multi-Radar Multi-Sensor (MRMS) QPE system. A new method is proposed and called PRORATE for probabilistic QPE using radar observations of rate and typology estimates. Probability distributions of precipitation rates are computed instead of deterministic values using a model quantifying the relation between radar reflectivity and the corresponding ''true'' precipitation. The model acknowledges the uncertainty arising from many factors operative at the radar measurement scale and from the correction algorithm. Ensembles of reflectivity-to-precipitation rate relationships accounting explicitly for precipitation typology were derived at a 5 min/1 km scale. This approach conditions probabilistic quantitative precipitation estimates (PQPE) on the precipitation rate and type. The model components were estimated on the basis of a 1 year long data sample over the CONUS. This PQPE model provides the basis for precipitation probability maps and the generation of radar precipitation ensembles. Maps of the precipitation exceedance probability for specific thresholds (e.g., precipitation return periods) are computed. Precipitation probability maps are accumulated to the hourly time scale and compare favorably to the deterministic QPE. As an essential property of precipitation, the impact of the temporal correlation on the hourly accumulation is examined. This approach to PQPE can readily apply to other systems including space-based passive and active sensor algorithms.

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

Cited by 72 publications

References 91 publications

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

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

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

Probabilistic precipitation rate estimates with ground‐based radar networks

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