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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F19%3A81516" target="_blank" >RIV/60460709:41330/19:81516 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0309170819304671?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0309170819304671?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.advwatres.2019.103448" target="_blank" >10.1016/j.advwatres.2019.103448</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

Extremes are rare and unexpected. This limits observations and constrains our knowledge on their predictability and behavior. Graphical tools are among the many methods developed to study extremes. A major weakness is that they rely on visual-inspection inferences which are subjective and make applications to large datasets time consuming and impractical. Here, we advance a graphical method, the so-called Mean Excess Function (MEF), into an algorithmic procedure. MEF investigates the mean value of a variable over threshold, and thus, focuses on extremes. We formulate precise and easy to apply statistical tests, based on the MEF, to assess if observed data can be described by exponential or heavier tails. As a real-world example, we apply our method in 21,348 daily precipitation records from all over the globe. Results show that the exponential tail hypothesis is rejected in 75,8% of the records indicating that heavy-tail distributions (alternative hypothesis) can better describe rainfall extremes. Th

Název v anglickém jazyce

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

Popis výsledku anglicky

Extremes are rare and unexpected. This limits observations and constrains our knowledge on their predictability and behavior. Graphical tools are among the many methods developed to study extremes. A major weakness is that they rely on visual-inspection inferences which are subjective and make applications to large datasets time consuming and impractical. Here, we advance a graphical method, the so-called Mean Excess Function (MEF), into an algorithmic procedure. MEF investigates the mean value of a variable over threshold, and thus, focuses on extremes. We formulate precise and easy to apply statistical tests, based on the MEF, to assess if observed data can be described by exponential or heavier tails. As a real-world example, we apply our method in 21,348 daily precipitation records from all over the globe. Results show that the exponential tail hypothesis is rejected in 75,8% of the records indicating that heavy-tail distributions (alternative hypothesis) can better describe rainfall extremes. Th

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10503 - Water resources

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

ADVANCES IN WATER RESOURCES

ISSN

0309-1708

e-ISSN

1872-9657

Svazek periodika

134

Číslo periodika v rámci svazku

N

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

9

Strana od-do

1-9

Kód UT WoS článku

000496256900014

EID výsledku v databázi Scopus

2-s2.0-85074275323