Estimations of Initial Errors Growth in Weather Prediction by Low-dimensional Atmospheric Model
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Estimations of Initial Errors Growth in Weather Prediction by Low-dimensional Atmospheric Model
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Advances in Intelligent Systems and Computing
ISSN
2194-5357
e-ISSN
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Volume of the periodical
2014
Issue of the periodical within the volume
289
Country of publishing house
CH - SWITZERLAND
Number of pages
10
Pages from-to
11-20
UT code for WoS article
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EID of the result in the Scopus database
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