An Assessment of the Method for Changepoint Detection Applied in Tropospheric Parameter Time Series given from Numerical Weather Model

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00025615%3A_____%2F20%3AN0000040" target="_blank" >RIV/00025615:_____/20:N0000040 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21110/20:00342415

Výsledek na webu

<a href="https://www.irsm.cas.cz/index_en.php?page=acta_detail_doi&id=338" target="_blank" >https://www.irsm.cas.cz/index_en.php?page=acta_detail_doi&id=338</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.13168/AGG.2020.0007" target="_blank" >10.13168/AGG.2020.0007</a>

Jazyk výsledku

angličtina

Název v původním jazyce

An Assessment of the Method for Changepoint Detection Applied in Tropospheric Parameter Time Series given from Numerical Weather Model

Popis výsledku v původním jazyce

We are discussing changepoint detection in tropospheric parameter time series that occurs in a numerical weather reanalysis model. Our approach applies a statistical method that is based on the maximum value of two sample t-statistics. We use critical values calculated by applying an asymptotic distribution. We also apply an asymptotic distribution to finding approximate critical values for the changepoint position. Experiments on “test” and “real” data illustrate the assumed accuracy and efficiency of our method. The method is assessed by its application to our series after adding synthetic shifts. A total of more than 3,000 original profiles are then analysed within the time-span of the years 1990-2015. The analysis shows that at least one changepoint is present in more than 9% of the studied original time series. The uncertainty of estimated times achieved tens of days for shifts larger than 9 mm, but it was increased up to hundreds of days in the case of smaller synthetic shifts. Discussed statistical method has potential for suspected change point detection in time series with higher time resolution.

Název v anglickém jazyce

An Assessment of the Method for Changepoint Detection Applied in Tropospheric Parameter Time Series given from Numerical Weather Model

Popis výsledku anglicky

We are discussing changepoint detection in tropospheric parameter time series that occurs in a numerical weather reanalysis model. Our approach applies a statistical method that is based on the maximum value of two sample t-statistics. We use critical values calculated by applying an asymptotic distribution. We also apply an asymptotic distribution to finding approximate critical values for the changepoint position. Experiments on “test” and “real” data illustrate the assumed accuracy and efficiency of our method. The method is assessed by its application to our series after adding synthetic shifts. A total of more than 3,000 original profiles are then analysed within the time-span of the years 1990-2015. The analysis shows that at least one changepoint is present in more than 9% of the studied original time series. The uncertainty of estimated times achieved tens of days for shifts larger than 9 mm, but it was increased up to hundreds of days in the case of smaller synthetic shifts. Discussed statistical method has potential for suspected change point detection in time series with higher time resolution.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10102 - Applied mathematics

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Acta Geodynamica et Geomaterialia

ISSN

1214-9705

e-ISSN

—

Svazek periodika

17

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

12

Strana od-do

101–112

Kód UT WoS článku

000520840100008

EID výsledku v databázi Scopus

2-s2.0-85082051116