Titlebook: Extremes in a Changing Climate; Detection, Analysis Amir AghaKouchak,David Easterling,Soroosh Sorooshi Book 2013 Springer Science+Business

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情感脆弱

Gian Paolo Clemente,Nino Savelliruction of regional non-stationary models. Such models are based on the concept of “regional parameters”, i.e. parameters being assumed identical for all sites within a homogeneous region. The inference of regional models poses an additional difficulty compared to the at-site approach: the existence

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Consequences for Insurance Workers,ltivariate Return Periods, quantiles, and design events, which represent quantities of utmost interest in applications, is rather tricky. In this Chapter we show how the use of Copulas may help in dealing with (and, possibly, solving) these problems.

crescendo

Catheter

https://doi.org/10.1007/978-3-030-38185-1n the predominant time of observation are provided. Sampling uncertainties are greater for precipitation than temperature extremes, because precipitation extremes are smaller in scale. A Monte Carlo approach to quantify sampling uncertainty is described, based on station distribution in the U.S. coo

故意釣到白楊

故意釣到白楊

Jacoby Adeshei Carter,Darryl Scrivennd the use of covariates. A changing climate may prove impetus to change some of the existing paradigms and explore new avenues. The need to reduce uncertainty, or alternatively derive more reliable uncertainty estimates, is exacerbated in a changing climate. One of the key strategies should be a mo

hardheaded

Bayesian Methods for Non-stationary Extreme Value Analysis,ruction of regional non-stationary models. Such models are based on the concept of “regional parameters”, i.e. parameters being assumed identical for all sites within a homogeneous region. The inference of regional models poses an additional difficulty compared to the at-site approach: the existence

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