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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

  • Czech description

Classification

  • Type

    J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

  • CEP classification

    IN - Informatics

  • OECD FORD branch

Result continuities

  • Project

  • 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

  • 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

  • EID of the result in the Scopus database