Clustering numerical weather forecasts to obtain statistical prediction intervals

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/00216275:25530/14:39898701

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Clustering numerical weather forecasts to obtain statistical prediction intervals

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

Clustering numerical weather forecasts to obtain statistical prediction intervals

Popis výsledku anglicky

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

Druh

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

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

<a href="/cs/project/EE2.3.30.0058" target="_blank" >EE2.3.30.0058: Rozvoj kvalitních vědeckovýzkumných týmů na Univerzitě Pardubice</a><br>

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

Meteorological Applications

ISSN

1350-4827

e-ISSN

—

Svazek periodika

21

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

14

Strana od-do

605-618

Kód UT WoS článku

000339954700016

EID výsledku v databázi Scopus

2-s2.0-84904763819