Assessment of temporal change in the tails of probability distribution of daily precipitation over India due to climatic shift in the 1970s
Neha Gupta,Sagar Rohidas Chavan +1 more
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In this article, a modified Probability Ratio Mean Square Error norm is used to identify the best-fit distribution to the tails of daily precipitation among four theoretical distributions (e.g., Pareto-type II, Lognormal, Weibull, and Gamma distributions).Abstract:
\n Daily precipitation extremes are crucial in the hydrological design of major water control structures and are expected to show a changing tendency over time due to climate change. The magnitude and frequency of extreme precipitation can be assessed by studying the upper tail behavior of probability distributions of daily precipitation. Depending on the tail behavior, the distributions can be classified into two categories: heavy-tailed and light-tailed distributions. Heavier tails indicate more frequent occurrences of extreme precipitation events. In this paper, we have analyzed the temporal change in the tail behavior of daily precipitation over India from pre- to post-1970 time periods as per the global climatic shift. A modified Probability Ratio Mean Square Error norm is used to identify the best-fit distribution to the tails of daily precipitation among four theoretical distributions (e.g., Pareto-type II, Lognormal, Weibull, and Gamma distributions). The results indicate that the Lognormal distribution, which is a heavy-tailed distribution, fits the tails of daily precipitation for the majority of the grids. It is inferred from the study that there is an increase in the heaviness of tails of daily precipitation data over India from pre- to post-1970 time periods.read more
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Organic molecules in the Sheepbed Mudstone, Gale Crater, Mars
Caroline Freissinet,Caroline Freissinet,Daniel P. Glavin,Paul R. Mahaffy,Kristen E. Miller,Jennifer L. Eigenbrode,Roger E. Summons,A. E. Brunner,A. E. Brunner,Arnaud Buch,Cyril Szopa,P. D. Archer,H. B. Franz,H. B. Franz,Sushil K. Atreya,William B. Brinckerhoff,Michel Cabane,Patrice Coll,Pamela G. Conrad,D. J. Des Marais,Jason P. Dworkin,Alberto G. Fairén,Alberto G. Fairén,Pascaline Francois,John P. Grotzinger,S. Kashyap,S. Kashyap,I. L. ten Kate,Laurie A. Leshin,C. A. Malespin,C. A. Malespin,M. G. Martin,M. G. Martin,F. J. Martin-Torres,F. J. Martin-Torres,Amy McAdam,Douglas W. Ming,Rafael Navarro-González,Alexander A. Pavlov,B. D. Prats,Steven W. Squyres,Andrew Steele,Jennifer C. Stern,Dawn Y. Sumner,Brad Sutter,María Paz Zorzano +45 more
TL;DR: The SAM gas chromatograph mass spectrometer (GCMS) was used to detect chlorobenzene and dichloroalkanes in the direct evolved gas analysis (EGA) mode in multiple portions of the fines from the Cumberland drill hole in the Sheepbed mudstone at Yellowknife Bay as mentioned in this paper.
Journal ArticleDOI
SPI-Based Drought Classification in Italy: Influence of Different Probability Distribution Functions
TL;DR: In this article , the standard approach to estimate the Standardized Precipitation Index (SPI) based on the Gamma probability distribution function, assessing the fitting performance of different biparametric distribution laws to monthly precipitation data.
Book ChapterDOI
Atmospheric extremes
TL;DR: In this paper , the authors focus on whether anthropogenic forcing has been sufficient for the extreme to emerge from the background noise of internal variability and, if so, by how much.
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Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Book
An Introduction to Statistical Modeling of Extreme Values
TL;DR: This paper presents a meta-modelling framework that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process of manually cataloging and modeling extreme value values in sequences.
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Modelling Extremal Events: for Insurance and Finance
TL;DR: In this article, an approach to Extremes via Point Processes is presented, and statistical methods for Extremal Events are presented. But the approach is limited to time series analysis for heavy-tailed processes.
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