Stochastic simulation of intermittent rainfall using the concept of “dry drift”

Schleiss, Marc; Chamoun, Sabine; Berne, Alexis

doi:10.1002/2013wr014641

Cited by 26 publications

(24 citation statements)

References 47 publications

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“…By replacing the kriging process with a sequential conditional Gaussian simulation approach (e.g. Pebesma, ; Schleiss et al , ), multiple similar realisations of the DSD can be simulated. In contrast to interpolation, in which the most likely value is found for each point, stochastic simulation produces many equally likely realisations of the field, all of which have identical spatial properties (variograms).…”

Section: Stochastic Simulation Of the Dsdmentioning

confidence: 99%

Spatial interpolation of experimental raindrop size distribution spectra

Raupach

Berne

2016

Quart J Royal Meteoro Soc

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We present a new approach for spatial interpolation of experimental raindrop size distribution (DSD) spectra. The DSD is fundamental to the study and understanding of precipitation and its monitoring and modelling. It is measured insitu using disdrometers at point locations. Disdrometers provide a (non‐parametric) DSD spectrum in which drop concentrations are provided per class of drop diameter. Our approach uses geostatistics to estimate the same non‐parametric DSD at unmeasured locations. Non‐stationarity due to intermittency is taken into account through estimation of the dry drift of drop concentrations, using a rain occurrence field. Principal component analysis is used to express the DSD spectra in terms of uncorrelated components that can be interpolated independently at a requested point. These interpolated components can then be recombined into the full DSD. Estimation uncertainty for the interpolated DSD spectra is provided. Because all bulk rainfall variables can be calculated from the DSD and the entire DSD is estimated, the technique effectively interpolates all bulk variables at once. Leave‐one‐out testing shows that the technique estimates the DSD with minimal bias, and it is shown that the technique can be easily adapted to perform stochastic simulation of the non‐parametric DSD.

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Section: Stochastic Simulation Of the Dsdmentioning

confidence: 99%

Spatial interpolation of experimental raindrop size distribution spectra

Raupach

Berne

2016

Quart J Royal Meteoro Soc

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“…In the context of precipitation, Leblois and Creutin (2013) adapted the TBM to simulate unconditional stochastic fields, which reproduce the correct intermittency and advection of precipitation fields, which was further extended for ensemble precipitation nowcasting by Caseri et al (2016). Schleiss et al (2014) also followed the geostatistical approach by using sequential Gaussian simulations (SGS) to generate conditional and unconditional radar rainfall fields, which realistically decay towards zero when moving out from the wet regions (concept of "dry drift"). Despite the speed-up strategies implemented, both the SGS and TBM approaches are still too computationally demanding to generate large precipitation ensembles over extended domains for real-time applications.…”

Section: Brief Review Of Spatial Stochastic Rainfall Generatorsmentioning

confidence: 99%

“…A similar method attempts to match the observed mean and standard deviation of the precipitation field (Seed et al, 1999;Niemi et al, 2014). More sophisticated methods apply a quantile-quantile transformation to match exactly the same empirical distribution of observed precipitation values, which is also known as probability matching or anamorphosis (Metta et al, 2009;Schleiss et al, 2014).…”

Section: Stochastic Rainfall Fields Using Local Noise Adjustmentmentioning

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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“…Another model dealing with rainfall intermittency considers two separated random fields. They account for rainfall occurrence and rain accumulation, respectively . A binary random field is first generated to represent the rain occurrence.…”

Section: Rainfall Generation By Geostatistical Simulationsmentioning

confidence: 99%

“…The truncated multivariate Gaussian field model naturally accommodates this smooth transition between dry and wet areas . This is not the case for the model with two independent random fields, although it can be refined to accommodate this rain decay by adding a deterministic drift to the multivariate Gaussian field that models rain accumulation (also called ‘Dry Drift’) …”

Section: Rainfall Generation By Geostatistical Simulationsmentioning

confidence: 99%

Generating synthetic rainfall with geostatistical simulations

Benoît

Mariethoz

2017

WIREs Water

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Rainfall is an important driver of many Earth surface and subsurface processes such as floods, groundwater recharge, or plants growth. Models are used to investigate the physical response of different environmental aspects to a range of possible rainfall events. To provide meaningful outputs, such models require realistic inputs. However, a major challenge in these models is the representation of the chaotic behavior of rainfall as well as its high temporal and spatial variability. The primary sources of information about rainfall are rainfall measurements, numerical weather models and climate models. Because these sources of information are incomplete and uncertain, stochastic models have been developed to augment available data using statistical methods. Applications to rainfall modeling include interpolation of rainfall measurements, downscaling of numerical model outputs, or weather generators. In this study, we focus on geostatistical stochastic rainfall generation models, which aim at characterizing and reproducing the spatial structure of rainfall. To this end, the different steps of the geostatistical rainfall modeling are reviewed. The spatial structure of the rain is first characterized by variogram analysis of rainfall data. Then, geostatistical models are discussed that match the observed rain structure. Finally, geostatistical simulations are applied to the inferred models for the generation of synthetic rain fields. WIREs Water 2017, 4:e1199. doi: 10.1002/wat2.1199 This article is categorized under: Science of Water > Hydrological Processes Science of Water > Methods

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Stochastic simulation of intermittent rainfall using the concept of “dry drift”

Cited by 26 publications

References 47 publications

Spatial interpolation of experimental raindrop size distribution spectra

Spatial interpolation of experimental raindrop size distribution spectra

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

Generating synthetic rainfall with geostatistical simulations

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