On the link between natural emergence and manifestation of a fundamental non-Gaussian geostatistical property: Asymmetry

Guthke, Philipp; Bàrdossy, András

doi:10.1016/j.spasta.2017.01.003

Cited by 12 publications

(8 citation statements)

References 15 publications

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“…Thanks to copulas, the behavior of a compound event can be conveniently decomposed into the marginal effects, given by the individual variables, and the linkage effects, as described by the copula uniquely associated with the involved (continuous) variables. As underlined in Guthke and Bárdossy (2017), besides taking into account non-Gaussianity, the main advantage of copula-based methods to treat geostatistical problems is the ''descriptive power and standardized interpretability''.…”

Section: Introductionmentioning

confidence: 99%

Clustering of concurrent flood risks via Hazard Scenarios

et al. 2018

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Section: Introductionmentioning

confidence: 99%

Clustering of concurrent flood risks via Hazard Scenarios

et al. 2018

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“…Despite the various sources of error, weather radar has been widely acknowledged as a valid indicator of precipitation patterns (e.g., Mendez Antonio et al, 2009;Fabry, 2015). Considering the pros and cons of the two most usual sources of precipitation information, the QPE obtained by merging the point-wise rain gauge observations and the radar-indicated precipitation pattern has become a research problem in both meteorology and hydrology (Hasan et al, 2016;Yan and Bárdossy, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…The common goal of these methods is to ensure that the simulated realizations comply with the additional information available (Lauzon and Marcotte, 2019). The additional information could be observed values of the simulated targets, measurements that are related linearly or nonlinearly to the simulated targets, third-or higher-order statistics (Guthke and Bárdossy, 2017;Bárdossy and Hörning, 2017), etc.…”

Section: Introductionmentioning

confidence: 99%

Conditional simulation of surface rainfall fields using modified phase annealing

Yan

Bàrdossy

Hörning

et al. 2020

Hydrol. Earth Syst. Sci.

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Abstract. The accuracy of quantitative precipitation estimation (QPE) over a given region and period is of vital importance across multiple domains and disciplines. However, due to the intricate temporospatial variability and the intermittent nature of precipitation, it is challenging to obtain QPE with adequate accuracy. This paper aims to simulate rainfall fields while honoring both the local constraints imposed by the point-wise rain gauge observations and the global constraints imposed by the field measurements obtained from weather radar. The conditional simulation method employed in this study is modified phase annealing (PA), which is practically an evolution from the traditional simulated annealing (SA). Yet unlike SA, which implements perturbations in the spatial field, PA implements perturbations in Fourier space, making it superior to SA in many respects. PA is developed in two ways. First, taking advantage of the global characteristic of PA, the method is only used to deal with global constraints, and the local ones are handed over to residual kriging. Second, except for the system temperature, the number of perturbed phases is also annealed during the simulation process, making the influence of the perturbation more global at initial phases and decreasing the global impact of the perturbation gradually as the number of perturbed phases decreases. The proposed method is used to simulate the rainfall field for a 30 min event using different scenarios: with and without integrating the uncertainty of the radar-indicated rainfall pattern and with different objective functions.

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“…They have been extensively used for modelling uncertainty of different types, from probabilistic methods (see Joe, 2015;Nelsen, 2006) to imprecise probabilities and decision theory (see Yager, 2013;Klement et al, 2014;Montes et al, 2015). Nowadays, copula-based models are also frequently used in many problems from spatial statistics; (see, e.g., Bárdossy & Li, 2008;Durante & Salvadori, 2010;Kazianka & Pilz, 2010;Guthke & Bárdossy, 2017).…”

Section: Introductionmentioning

confidence: 99%

Copula-based fuzzy clustering of spatial time series

2017

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This paper contributes to the existing literature on the analysis of spatial time series presenting a new clustering algorithm called COFUST, i.e. COpula-based FUzzy clustering algorithm for Spatial Time series. The underlying idea of this algorithm is to perform a fuzzy Partitioning Around Medoids (PAM) clustering using copula-based approach to interpret comovements of time series. This generalisation allows both to extend usual clustering methods for time series based on Pearson's correlation and to capture the uncertainty that arises assigning units to clusters. Furthermore, its flexibility permits to include directly in the algorithm the spatial information. Our approach is presented and discussed using both simulated and real data, highlighting its main advantages.

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On the link between natural emergence and manifestation of a fundamental non-Gaussian geostatistical property: Asymmetry

Cited by 12 publications

References 15 publications

Clustering of concurrent flood risks via Hazard Scenarios

Clustering of concurrent flood risks via Hazard Scenarios

Conditional simulation of surface rainfall fields using modified phase annealing

Copula-based fuzzy clustering of spatial time series

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