Initial Error Growth and Predictability of Chaotic Low-dimensional Atmospheric Model

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F14%3A10292083" target="_blank" >RIV/00216208:11320/14:10292083 - isvavai.cz</a>

Výsledek na webu

<a href="http://link.springer.com/article/10.1007/s11633-014-0788-3" target="_blank" >http://link.springer.com/article/10.1007/s11633-014-0788-3</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11633-014-0788-3" target="_blank" >10.1007/s11633-014-0788-3</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Initial Error Growth and Predictability of Chaotic Low-dimensional Atmospheric Model

Popis výsledku v původním jazyce

The growth of small errors in weather prediction is exponential on average. As an error becomes larger, its growth slows down and then stops with the magnitude of the error saturating at about the average distance between two states chosen randomly. Thispaper studies the error growth in a low-dimensional atmospheric model before, during and after the initial exponential divergence occurs. We test cubic, quartic and logarithmic hypotheses by ensemble prediction method. Furthermore, the quadratic hypothesis suggested by Lorenz in 1969 is compared with the ensemble prediction method. The study shows that a small error growth is best modeled by the quadratic hypothesis. After the error exceeds about a half of the average value of variables, logarithmic approximation becomes superior. It is also shown that the time length of the exponential growth in the model data is a function of the size of small initial error and the largest Lyapunov exponent. We conclude that the size of the error at

Název v anglickém jazyce

Initial Error Growth and Predictability of Chaotic Low-dimensional Atmospheric Model

Popis výsledku anglicky

The growth of small errors in weather prediction is exponential on average. As an error becomes larger, its growth slows down and then stops with the magnitude of the error saturating at about the average distance between two states chosen randomly. Thispaper studies the error growth in a low-dimensional atmospheric model before, during and after the initial exponential divergence occurs. We test cubic, quartic and logarithmic hypotheses by ensemble prediction method. Furthermore, the quadratic hypothesis suggested by Lorenz in 1969 is compared with the ensemble prediction method. The study shows that a small error growth is best modeled by the quadratic hypothesis. After the error exceeds about a half of the average value of variables, logarithmic approximation becomes superior. It is also shown that the time length of the exponential growth in the model data is a function of the size of small initial error and the largest Lyapunov exponent. We conclude that the size of the error at

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název periodika

International Journal of Automation and Computing

ISSN

1476-8186

e-ISSN

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Svazek periodika

11

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

9

Strana od-do

256-264

Kód UT WoS článku

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EID výsledku v databázi Scopus

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