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

Nerini, Daniele; Bešič, Nikola; Sideris, Ioannis V.; Germann, Urs; Foresti, Loris

doi:10.5194/hess-21-2777-2017

Cited by 59 publications

(52 citation statements)

References 62 publications

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“…Overcoming the assumption of global stationarity without internal boundaries would be possible, using formal nonstationary spatial modeling or models where the covariance parameters are smoothly varying in space. Examples of such more advanced approaches are Lindgren and Rue (2015), Nerini et al (2017), Nychka et al (2018), and Risser and Calder (2017). These techniques are, in general, technically complex and computationally demanding, but there is scope for overcoming a current limitation of our method.…”

Section: Discussionmentioning

confidence: 99%

Ensemble Spatial Precipitation Analysis From Rain Gauge Data: Methodology and Application in the European Alps

Frei

Isotta

2019

JGR Atmospheres

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The deterministic nature of conventional precipitation data sets complicates their utility in applications. The underlying estimation principle (minimizing errors) involves biases in extremes, and conventional uncertainty quantification (cross‐validation) is impractical. We present a method to derive spatial analyses of daily precipitation, probabilistically, as an ensemble, conditional on the available rain gauge data. The method builds on and extends previous techniques using conditional simulation with Gaussian Random Fields. The extension involves trans‐Gaussian and piecewise covariance modeling to let the ensemble respond to regional precipitation conditions, Bayesian inference to account for parameter uncertainty, and simulation on a primary grid to ensure the ensemble has a well‐defined areal support. While addressing prevalent issues of existing techniques, our method involves a compromise in global consistency that limits the utility of the ensemble over large domains. We apply the method to derive ensembles of area‐mean precipitation for hydrological area units in the Alps. The ensembles are demonstrated to plausibly capture variations in uncertainty with rainfall condition, rain gauge density, and averaging area. Evaluations suggest that the probabilistic estimates are internally consistent and of good statistical reliability. There is a tendency to underestimate uncertainty for light precipitation. Results point to remarkable uncertainties, even with the dense gauge networks in the Alps: For means over 500‐km2 areas we find the ensemble spread to be typically a factor of 2–4 (factor of 1.5) for intense convective (stratiform) events. This also implies nonnegligible uncertainty in climate indices. Probabilistic representation of interpolation uncertainty in spatial data sets allows users to track them into applications.

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Section: Discussionmentioning

confidence: 99%

Ensemble Spatial Precipitation Analysis From Rain Gauge Data: Methodology and Application in the European Alps

Frei

Isotta

2019

JGR Atmospheres

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“…For subsequent applications, we assume that the rain intensity can be simulated conditional to rain types. This is the classical aim of space-time distributed stochastic rainfall generators, which are becoming more and more common to address the needs of high-resolution hydrometeorological impact studies (Leblois and Creutin, 2013;Paschalis et al, 2013;Nerini et al, 2017;Benoit et al, 2018a). Hence, two main applications can be considered for the stochastic rain type simulation.…”

Section: Discussionmentioning

confidence: 99%

“…Secondly, the conditioning of the stochastic rain type model to the state of the atmosphere preserves the relationships between rain type occurrence and the value of meteorological covariates, which ensures the climatological coherence of the stochastic weather generator. Once realistic rain type time series have been simulated, high-resolution rain fields can be simulated conditional to rain types using any high-resolution stochastic rainfall generator 30 (Leblois and Creutin, 2013;Paschalis et al, 2013;Nerini et al, 2017;Benoit et al, 2018a).…”

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confidence: 99%

Accounting for rain type non-stationarity in sub-daily stochastic weather generators

Benoît

Vrac

Mariethoz

2019

Preprint

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At sub-daily resolution, rain intensity exhibits a strong variability in space and time, which is favorably modelled using stochastic approaches. This strong variability is further enhanced because of the diversity of processes that produce rain (e.g. frontal storms, mesoscale convective systems, local convection), which results in a multiplicity of space-time patterns embedded into rain fields, and in turn leads to non-stationarity of rain statistics. To account for this non-stationarity in the context of stochastic weather generators, and therefore preserve the climatological coherence of rain simulations, we propose 5 to resort to rain types simulation.We explore two methods to simulate rain type time series conditional to meteorological covariates: a parametric approach based on a non-homogeneous semi-Markov chain, and a non-parametric approach based on multiple-point statistics. Both methods are tested by cross-validation using a 17-year long rain type time series defined over central Germany. Evaluation results indicate that the non-parametric approach better simulates the relationships between rain types and meteorological covariates. 10Indeed, the inherent simplifications in the parametric model do not allow fully resolving complex and non-linear interactions between the rainfall statistics and meteorological covariates.The proposed approach is applied to generate rain type time series conditional to meteorological covariates simulated by a Regional Climate Model under an RCP8.5 emission scenario. Results indicate that, by the end of the century, the distribution of rain types could be modified over the area of interest, with an increased frequency of convective-and frontal-like rains at 15 the expense of more stratiform events.

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“…Such predictions can then be used in the context of ensemble precipitation nowcasting to inform the stochastic generators of precipitation fields about local trends induced by growth and decay (e.g. Nerini et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

A 10‐year radar‐based analysis of orographic precipitation growth and decay patterns over the Swiss Alpine region

Foresti

Sideris

Panziera

et al. 2018

Quart J Royal Meteoro Soc

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The dependence of precipitation growth and decay on the orientation of orographic features, mesoscale flow and freezing‐level height is quantified using a 10‐year archive of composite weather radar images over the Swiss Alpine region. The mesoscale flow is described by the motion of radar precipitation echoes, computed through variational echo tracking, while the freezing‐level height is extracted from the analyses of the numerical weather prediction model COSMO. On the northern side of the Alps, the areas of growth are generally observed on the upwind slopes of the Alpine chain, while on the southern side their location also depends on factors other than the motion of precipitation, such as the convergence of low‐level flows. On the other hand, the decay of precipitation is generally found in the inner Alpine valleys and on the downwind slopes of the Alpine chain. Compared to situations characterized by low freezing level, the areas of precipitation growth penetrate more into the mountain range with high‐freezing‐level conditions. When considering a time lag of 1 h and specific flow conditions, the systematic growth and decay of precipitation can explain up to 30–40% of its total variability over the orography. The relative contribution to the total variability is lower with high freezing level because the non‐systematic variability is larger. The statistical analysis of precipitation growth and decay using large archives of composite radar images highlights the mesoscale flow conditions and geographical locations most prone to orographic precipitation enhancement, but also has potential to improve existing precipitation nowcasting systems in mountainous regions and to provide a basis for the verification of systematic biases of numerical weather prediction models.

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

Cited by 59 publications

References 62 publications

Ensemble Spatial Precipitation Analysis From Rain Gauge Data: Methodology and Application in the European Alps

Ensemble Spatial Precipitation Analysis From Rain Gauge Data: Methodology and Application in the European Alps

Accounting for rain type non-stationarity in sub-daily stochastic weather generators

A 10‐year radar‐based analysis of orographic precipitation growth and decay patterns over the Swiss Alpine region

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