Estimations of Initial Errors Growth in Weather Prediction by Low-dimensional Atmospheric Model

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F14%3A10292089" target="_blank" >RIV/00216208:11320/14:10292089 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.springer.com/gp/book/9783319074009" target="_blank" >http://www.springer.com/gp/book/9783319074009</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-07401-6_2" target="_blank" >10.1007/978-3-319-07401-6_2</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Estimations of Initial Errors Growth in Weather Prediction by Low-dimensional Atmospheric Model

Popis výsledku v původním jazyce

Initial errors in weather prediction grow in time. As errors become larger, their growth slows down and then stops at an asymptotic value. Time of reaching this value represents the limit of predictability. Other time limits that measure the error growthare doubling time ?d, and times when the forecast error reaches 95%, 71%, 50%, and 25% of the limit of predictability. This paper studies asymptotic value and time limits in a low-dimensional atmospheric model for five initial errors, using ensemble prediction method as well as error approximation by quadratic and logarithmic hypothesis. We show that quadratic hypothesis approximates the model data better for almost all initial errors and time lengths. We also demonstrate that both hypotheses can be further improved to achieve even better match of the asymptotic value and time limits with the model.

Název v anglickém jazyce

Estimations of Initial Errors Growth in Weather Prediction by Low-dimensional Atmospheric Model

Popis výsledku anglicky

Initial errors in weather prediction grow in time. As errors become larger, their growth slows down and then stops at an asymptotic value. Time of reaching this value represents the limit of predictability. Other time limits that measure the error growthare doubling time ?d, and times when the forecast error reaches 95%, 71%, 50%, and 25% of the limit of predictability. This paper studies asymptotic value and time limits in a low-dimensional atmospheric model for five initial errors, using ensemble prediction method as well as error approximation by quadratic and logarithmic hypothesis. We show that quadratic hypothesis approximates the model data better for almost all initial errors and time lengths. We also demonstrate that both hypotheses can be further improved to achieve even better match of the asymptotic value and time limits with the model.

Druh

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

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

—

Návaznosti

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

Advances in Intelligent Systems and Computing

ISSN

2194-5357

e-ISSN

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Svazek periodika

2014

Číslo periodika v rámci svazku

289

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

10

Strana od-do

11-20

Kód UT WoS článku

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EID výsledku v databázi Scopus

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