Tails of extremes: Advancing a graphical method and harnessing big data to assess precipitation extremes

Nerantzaki, Sofia D.; Papalexiou, S.

doi:10.1016/j.advwatres.2019.103448

Cited by 52 publications

(37 citation statements)

References 42 publications

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“…Their results are in agreement with the findings of Nerantzaki and Papalexiou [38]. Nerantzaki and Papalexiou [38] suggested that light tail distribution should be used under caution because it might fail to capture extreme rainfall depending on the location as mentioned earlier. But Ethiopia is one of the East Africa highland countries.…”

Section: Amount Processsupporting

confidence: 90%

“…In contrast, it was suggested that exponential tails occur when precipitation is generated by similar weather systems, such as in central Mexican high plateau, the Brazilian Highlands, the East African Highlands and California [10]. Their results are in agreement with the findings of Nerantzaki and Papalexiou [38]. Nerantzaki and Papalexiou [38] suggested that light tail distribution should be used under caution because it might fail to capture extreme rainfall depending on the location as mentioned earlier.…”

Section: Amount Processsupporting

confidence: 74%

“…But Ethiopia is one of the East Africa highland countries. Hence, as supported by the results, light tail distributions could be appropriate [10,38]. In this work, three different probability distributions are used to model the magnitude of daily rainfall.…”

Section: Amount Processmentioning

confidence: 87%

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Pricing weather derivative using Markov Chain Analogue Year daily rainfall model

2020

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In this paper, a Markov Chain Analogue Year model is used to describe the occurrence process of daily rainfall and the amount of daily rainfall on wet days is presented using Gamma, Exponential and mixed Exponential distributions. Combining the occurrence and the amount process models together, we developed Markov Chain Analogue Year Gamma model, Markov Chain Analogue Year Exponential model and Markov Chain Analogue Year Mixed Exponential model. The result shows that all the three models perform very well in the simulation of precipitation process in the study area and their performances are nearly the same with a slight difference. Using the results of these models, option prices for Teff and wheat crops for different months in their growing season are calculated. The calculated option price has an excellent accuracy with maximum absolute error of 0.54.instance rainfall fluctuation alone could contribute a loss of 2 billion USD in the sector [6]. In general, agriculture takes the first rank in the Ethiopian economy and it is extremely dependent on natural rainfall [4,51]. Therefore, modeling and pricing rainfall derivative is very important for the country in order to reduce such risks. This paper has two main objectives, the first objective is to present a daily rainfall model and the second objective is to use the daily rainfall model to obtain rainfall index, that is, cumulative rainfall (CR) and based on the CR, we calculate an option price for rainfall derivatives (for Teff and wheat crops). The paper is structured as follows: in Sect. 2 we have discussed materials and methods, model formulation, major crops and their response to water stress and description of pricing formula for rainfall derivatives, in Sect. 3 the obtained results are discussed under result and discussion, and finally in Sect. 4 we have made some concluding remarks.

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Section: Amount Processsupporting

confidence: 90%

Section: Amount Processsupporting

confidence: 74%

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Pricing weather derivative using Markov Chain Analogue Year daily rainfall model

2020

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“…The frequency and the magnitude of extremes is quantified by the heaviness of the tail. Specifically, for precipitation extremes, recent global and large‐scale studies show that heavy‐tailed distributions represent extremes more accurately (see, e.g., Nerantzaki & Papalexiou, ; Papalexiou, AghaKouchak, et al, ; Serinaldi & Kilsby, ). In this respect, the flexibility offered by this scheme, allowing the choice of marginals with any asymptotic tail behavior (power type, stretched exponential, lognormal, etc.…”

Section: Discussionmentioning

confidence: 99%

Random Fields Simplified: Preserving Marginal Distributions, Correlations, and Intermittency, With Applications From Rainfall to Humidity

Papalexiou

Serinaldi

2020

Water Resources Research

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Nature manifests itself in space and time. The spatiotemporal complexity of processes such as precipitation, temperature, and wind, does not allow purely deterministic modeling. Spatiotemporal random fields have a long history in modeling such processes, and yet a single unified framework offering the flexibility to simulate processes that may differ profoundly does not exist. Here we introduce a blueprint to efficiently simulate spatiotemporal random fields that preserve any marginal distribution, any valid spatiotemporal correlation structure, and intermittency. We suggest a set of parsimonious yet flexible marginal distributions and provide a rule of thumb for their selection. We propose a new and unified approach to construct flexible spatiotemporal correlation structures by combining copulas and survival functions. The versatility of our framework is demonstrated by simulating conceptual cases of intermittent precipitation, double‐bounded relative humidity, and temperature maxima fields. As a real‐word case we simulate daily precipitation fields. In all cases, we reproduce the desired properties. In an era characterized by advances in remote sensing and increasing availability of spatiotemporal data, we deem that this unified approach offers a valuable and easy‐to‐apply tool for modeling complex spatiotemporal processes.

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“…In particular, their tail behavior is crucial for the estimation of large precipitation events. Most global studies with focus on the large amounts find tails heavier than exponential (Nerantzaki and Papalexiou 2019;Papalexiou et al 2013;Serinaldi and Kilsby 2014). By arguments from atmospheric physics, Wilson and Toumi (2005) deduced a stretched exponential tail with a universal shape parameter as an approximation for the extreme regime.…”

Section: Introductionmentioning

confidence: 99%

An ARFIMA-based model for daily precipitation amounts with direct access to fluctuations

Polotzek

Kantz

2020

Stoch Environ Res Risk Assess

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Correlations in models for daily precipitation are often generated by elaborate numerics that employ a high number of hidden parameters. We propose a parsimonious and parametric stochastic model for European mid-latitude daily precipitation amounts with focus on the influence of correlations on the statistics. Our method is meta-Gaussian by applying a truncated-Gaussian-power (tGp) transformation to a Gaussian ARFIMA model. The speciality of this approach is that ARFIMA(1, d, 0) processes provide synthetic time series with long- (LRC), meaning the sum of all autocorrelations is infinite, and short-range (SRC) correlations by only one parameter each. Our model requires the fit of only five parameters overall that have a clear interpretation. For model time series of finite length we deduce an effective sample size for the sample mean, whose variance is increased due to correlations. For example the statistical uncertainty of the mean daily amount of 103 years of daily records at the Fichtelberg mountain in Germany equals the one of about 14 years of independent daily data. Our effective sample size approach also yields theoretical confidence intervals for annual total amounts and allows for proper model validation in terms of the empirical mean and fluctuations of annual totals. We evaluate probability plots for the daily amounts, confidence intervals based on the effective sample size for the daily mean and annual totals, and the Mahalanobis distance for the annual maxima distribution. For reproducing annual maxima the way of fitting the marginal distribution is more crucial than the presence of correlations, which is the other way round for annual totals. Our alternative to rainfall simulation proves capable of modeling daily precipitation amounts as the statistics of a random selection of 20 data sets is well reproduced.

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Tails of extremes: Advancing a graphical method and harnessing big data to assess precipitation extremes

Cited by 52 publications

References 42 publications

Pricing weather derivative using Markov Chain Analogue Year daily rainfall model

Pricing weather derivative using Markov Chain Analogue Year daily rainfall model

Random Fields Simplified: Preserving Marginal Distributions, Correlations, and Intermittency, With Applications From Rainfall to Humidity

An ARFIMA-based model for daily precipitation amounts with direct access to fluctuations

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