Precise temporal Disaggregation Preserving Marginals and Correlations (DiPMaC) for stationary and nonstationary processes

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F18%3A77617" target="_blank" >RIV/60460709:41330/18:77617 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1029/2018WR022726" target="_blank" >http://dx.doi.org/10.1029/2018WR022726</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1029/2018WR022726" target="_blank" >10.1029/2018WR022726</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Precise temporal Disaggregation Preserving Marginals and Correlations (DiPMaC) for stationary and nonstationary processes

Popis výsledku v původním jazyce

Hydroclimatic variables such as precipitation and temperature are often measured or simulated by climate models at coarser spatiotemporal scales than those needed for operational purposes. This has motivated more than half a century of research in developing disaggregation methods that break down coarse-scale time series into finer scales, with two primary objectives: (a) reproducing the statistical properties of the fine-scale process and (b) preserving the original coarse-scale data. Existing methods either preserve a limited number of statistical moments at the fine scale, which is often insufficient and can lead to an unrepresentative approximation of the actual marginal distribution, or are based on a limited number of a priori distributional assumptions, for example, lognormal. Additionally, they are not able to account for potential nonstationarity in the underlying fine-scale process. Here we introduce a novel disaggregation method, named Disaggregation Preserving Marginals and Correlations (

Název v anglickém jazyce

Precise temporal Disaggregation Preserving Marginals and Correlations (DiPMaC) for stationary and nonstationary processes

Popis výsledku anglicky

Hydroclimatic variables such as precipitation and temperature are often measured or simulated by climate models at coarser spatiotemporal scales than those needed for operational purposes. This has motivated more than half a century of research in developing disaggregation methods that break down coarse-scale time series into finer scales, with two primary objectives: (a) reproducing the statistical properties of the fine-scale process and (b) preserving the original coarse-scale data. Existing methods either preserve a limited number of statistical moments at the fine scale, which is often insufficient and can lead to an unrepresentative approximation of the actual marginal distribution, or are based on a limited number of a priori distributional assumptions, for example, lognormal. Additionally, they are not able to account for potential nonstationarity in the underlying fine-scale process. Here we introduce a novel disaggregation method, named Disaggregation Preserving Marginals and Correlations (

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10501 - Hydrology

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

WATER RESOURCES RESEARCH

ISSN

0043-1397

e-ISSN

—

Svazek periodika

54

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

24

Strana od-do

7435-7458

Kód UT WoS článku

000450726000019

EID výsledku v databázi Scopus

2-s2.0-85053668767