Advancing Space‐Time Simulation of Random Fields: From Storms to Cyclones and Beyond

Papalexiou, S.; Serinaldi, Francesco; Porcu, Emilio

doi:10.1029/2020wr029466

Cited by 33 publications

(32 citation statements)

References 73 publications

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“…The main drawback of statistical downscaling is the simplification assumptions that can lead to divergences from the physical system under representation [47]. However, other more sophisticated methodologies, such as the Complete Stochastic Modeling Solution (CoSMos), could be applied for ramp forecasting [48]. Deep-learning technologies can also be applied to the post-processing of NWP results for wind-power applications [47,49].…”

Section: Discussionmentioning

confidence: 99%

Wind-Ramp Predictability

Pereyra-Castro

Caetano

2022

Atmosphere

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The intermittent nature of wind resources is challenging for their integration into the electrical system. The identification of weather systems and the accurate forecast of wind ramps can improve wind-energy management. In this study, extreme wind ramps were characterized at four different geographical sites in terms of duration, persistence, and weather system. Mid-latitude systems are the main cause of wind ramps in Mexico during winter. The associated ramps last around 3 h, but intense winds are sustained for up to 40 h. Storms cause extreme wind ramps in summer due to the downdraft contribution to the wind gust. Those events last about 1 to 3 h. Dynamic downscaling is computationally costly, and statistical techniques can improve wind forecasting. Evaluation of the North American Mesoscale Forecast System (NAM) operational model to simulate wind ramps and two bias-correction methods (simple bias and quantile mapping) was done for two selected sites. The statistical adjustment reduces the excess of no-ramps (≤|0.5| m/s) predicted by NAM compared to observed wind ramps. According to the contingency table-derived indices, the wind-ramp distribution correction with simple bias method or quantile mapping method improves the prediction of positive and negative ramps.

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Section: Discussionmentioning

confidence: 99%

Wind-Ramp Predictability

Pereyra-Castro

Caetano

2022

Atmosphere

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“…Possible reasons are: (a) complex expressions are not as favored as simpler two‐ or three‐parameter models that include a location parameter; (b) fitting challenges; (c) complicated or not analytical moments and L‐moments expressions, and (d) the need to use simple distributions in stochastic models to facilitate their mathematical formulation. Yet advances in stochastic modeling (Papalexiou, 2018; Papalexiou & Serinaldi, 2020; Papalexiou, Serinaldi, & Porcu, 2021), allow rainfall modeling with any desired distribution and correlation structure. Also, global studies (Papalexiou & Koutsoyiannis, 2012, 2016) analyzing thousands of records indicate that such distributions describe effectively nonzero rainfall.…”

Section: New System Of Probability Distributionsmentioning

confidence: 99%

“…Note that this modeling approach was applied in multivariate simulation using parametric cross‐CTFs (Papalexiou, 2018), in the DiPMaC disaggregation scheme and for nonstationary simulation (Papalexiou, Markonis, et al., 2018), and more recently, for static and Lagrangian spatiotemporal random fields in Papalexiou and Serinaldi (2020), Papalexiou, Serinaldi, & Porcu (2021), respectively. The same approach is modified here to couple processes with Bernoulli and continuous marginal distributions to improve simulation of intermittent processes and reproduce rainfall characteristics at a large range of scales.…”

Section: Correlation Transformations and Extensions To Negative Spacementioning

confidence: 99%

Rainfall Generation Revisited: Introducing CoSMoS‐2s and Advancing Copula‐Based Intermittent Time Series Modeling

Papalexiou

2022

Water Resources Research

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What elements should a parsimonious model reproduce at a single scale to precisely simulate rainfall at many scales? We posit these elements are: (a) the probability of dry and linear correlation structure of the wet/dry sequence as a proxy reproducing the distribution of wet/dry spells, and (b) the marginal distribution of nonzero rainfall and its correlation structure. We build a two‐state rainfall model, the CoSMoS‐2s, that explicitly reproduces these elements and is easily applicable at any timescale. Additionally, the paper: (a) introduces the Generalized Exponential (scriptGscriptE $\mathcal{G}\mathcal{E}$) distribution system comprising six flexible distributions with desired properties to describe nonzero rainfall and facilitate time series generation; (b) extends the CoSMoS framework to allow simulations with negative correlations; (c) simplifies the generation of binary sequences with any correlation structure by analytical approximations; (d) introduces the rank‐based CoSMoS‐2s that preserves Spearman's correlations, has an analytical formulation, and is also applicable for infinite variance time series, (e) introduces the copula‐based CoSMoS‐2s enabling intermittent times series generation with nonzero values having the dependence structure of any desired copula, and (f) offers conceptual generalizations for rainfall modeling and beyond, with specific ideas for future improvements and extensions. The CoSMoS‐2s is tested using four long hourly rainfall records; the simulations reproduce rainfall properties at multiple scales including the wet/dry spells, probability of dry, characteristics of nonzero rainfall, and the behavior of extremes.

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“…Binary sequences with specified serial and cross correlation, and rate of occurrence are simulated by BetaBit algorithm (Serinaldi and Lombardo 2017a) and its extension BetaBitST , which can be generalized by the CoSMoS framework (Papalexiou 2018;Papalexiou et al 2018;Papalexiou and Serinaldi 2020;Papalexiou et al 2021). In Appendix A, we suggest an extension of BetaBitST enabling the simulation of negatively cross correlated binary random processes, which are used in the discussion below.…”

Section: Pearson-based Modelingmentioning

confidence: 99%

Testing tests before testing data: an untold tale of compound events and binary dependence

Serinaldi

Lombardo

Kilsby

2022

Stoch Environ Res Risk Assess

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In any statistical investigation, we deal with the applications of probability theory to real problems, and the conclusions are inferences based on observations. To obtain plausible inferences, statistical analysis requires careful understanding of the underlying probabilistic model, which constrains the extraction and interpretation of information from observational data, and must be preliminarily checked under controlled conditions. However, these very first principles of statistical analysis are often neglected in favor of superficial and automatic application of increasingly available ready-to-use software, which might result in misleading conclusions, confusing the effect of model constraints with meaningful properties of the process of interest. To illustrate the consequences of this approach, we consider the emerging research area of so-called ‘compound events’, defined as a combination of multiple drivers and/or hazards that contribute to hydro-climatological risk. In particular, we perform an independent validation analysis of a statistical testing procedure applied to binary series describing the joint occurrence of hydro-climatological events or extreme values, which is supposed to be superior to classical analysis based on Pearson correlation coefficient. To this aim, we suggest a theoretically grounded model relying on Pearson correlation coefficient and marginal rates of occurrence, which enables accurate reproduction of the observed joint behavior of binary series, and offers a sound simulation tool useful for informing risk assessment procedures. Our discussion on compound events highlights the dangers of renaming known topics, using imprecise definitions and overlooking or misusing existing statistical methods. On the other hand, our model-based approach reveals that consistent statistical analyses should rely on informed stochastic modeling in order to avoid the proposal of flawed methods, and the untimely dismissal of well-devised theories.

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Advancing Space‐Time Simulation of Random Fields: From Storms to Cyclones and Beyond

Cited by 33 publications

References 73 publications

Wind-Ramp Predictability

Wind-Ramp Predictability

Rainfall Generation Revisited: Introducing CoSMoS‐2s and Advancing Copula‐Based Intermittent Time Series Modeling

Testing tests before testing data: an untold tale of compound events and binary dependence

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