All

What are you looking for?

All
Projects
Results
Organizations

Quick search

  • Projects supported by TA ČR
  • Excellent projects
  • Projects with the highest public support
  • Current projects

Smart search

  • That is how I find a specific +word
  • That is how I leave the -word out of the results
  • “That is how I can find the whole phrase”

Clustering numerical weather forecasts to obtain statistical prediction intervals

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F14%3A86096964" target="_blank" >RIV/61989100:27240/14:86096964 - isvavai.cz</a>

  • Alternative codes found

    RIV/00216275:25530/14:39898701

  • Result on the web

    <a href="http://onlinelibrary.wiley.com/doi/10.1002/met.1383/epdf" target="_blank" >http://onlinelibrary.wiley.com/doi/10.1002/met.1383/epdf</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1002/met.1383" target="_blank" >10.1002/met.1383</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Clustering numerical weather forecasts to obtain statistical prediction intervals

  • Original language description

    The numerical weather prediction (NWP) model outputs are point deterministic values arranged on a three-dimensional grid. However, there is always some level of uncertainty in the prediction. Many applications would benefit from provision of relevant uncertainty information along with the forecast. A common means of formulating and communicating forecast uncertainty are prediction intervals (PI). In this study, various methods for modelling the uncertainty of NWP forecasts are investigated and PIs provided for predictions accordingly. In particular, the interest is in analysing the historical performance of the system as a valuable source of information for uncertainty analysis. Various clustering algorithms are employed to group the performance records as the first step of the PI determination process. In the second step, a range of methods are used to fit appropriate probability distributions to errors of each cluster. As a result, PIs can be computed dynamically depending on the for

  • 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

    <a href="/en/project/EE2.3.30.0058" target="_blank" >EE2.3.30.0058: Development of Research Teams at the University of Pardubice</a><br>

  • 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

    Meteorological Applications

  • ISSN

    1350-4827

  • e-ISSN

  • Volume of the periodical

    21

  • Issue of the periodical within the volume

    3

  • Country of publishing house

    GB - UNITED KINGDOM

  • Number of pages

    14

  • Pages from-to

    605-618

  • UT code for WoS article

    000339954700016

  • EID of the result in the Scopus database

    2-s2.0-84904763819