The use of serially complete station data to improve the temporal continuity of gridded precipitation and temperature estimates

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F21%3A86953" target="_blank" >RIV/60460709:41330/21:86953 - isvavai.cz</a>

Výsledek na webu

<a href="https://journals.ametsoc.org/view/journals/hydr/aop/JHM-D-20-0313.1/JHM-D-20-0313.1.xml?tab_body=abstract-display" target="_blank" >https://journals.ametsoc.org/view/journals/hydr/aop/JHM-D-20-0313.1/JHM-D-20-0313.1.xml?tab_body=abstract-display</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1175/JHM-D-20-0313.1" target="_blank" >10.1175/JHM-D-20-0313.1</a>

Jazyk výsledku

angličtina

Název v původním jazyce

The use of serially complete station data to improve the temporal continuity of gridded precipitation and temperature estimates

Popis výsledku v původním jazyce

Stations are an important source of meteorological data, but often suffer from missing values and short observation periods. Gap filling is widely used to generate serially complete datasets (SCDs), which are subsequently used to produce gridded meteorological estimates. However, the value of SCDs in spatial interpolation is scarcely studied. Based on our recent efforts to develop a SCD over North America (SCDNA), we explore the extent to which gap filling improves gridded precipitation and temperature estimates. We address two specific questions: 1) Can SCDNA improve the statistical accuracy of gridded estimates in North America 2) Can SCDNA improve estimates of trends on gridded data In addressing these questions, we also evaluate the extent to which results depend on the spatial density of the station network and the spatial interpolation methods used. Results show that the improvement in statistical interpolation due to gap filling is more obvious for precipitation, followed by minimum temperatur

Název v anglickém jazyce

The use of serially complete station data to improve the temporal continuity of gridded precipitation and temperature estimates

Popis výsledku anglicky

Stations are an important source of meteorological data, but often suffer from missing values and short observation periods. Gap filling is widely used to generate serially complete datasets (SCDs), which are subsequently used to produce gridded meteorological estimates. However, the value of SCDs in spatial interpolation is scarcely studied. Based on our recent efforts to develop a SCD over North America (SCDNA), we explore the extent to which gap filling improves gridded precipitation and temperature estimates. We address two specific questions: 1) Can SCDNA improve the statistical accuracy of gridded estimates in North America 2) Can SCDNA improve estimates of trends on gridded data In addressing these questions, we also evaluate the extent to which results depend on the spatial density of the station network and the spatial interpolation methods used. Results show that the improvement in statistical interpolation due to gap filling is more obvious for precipitation, followed by minimum temperatur

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

2021

Kód důvěrnosti údajů

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

Název periodika

JOURNAL OF HYDROMETEOROLOGY

ISSN

1525-755X

e-ISSN

1525-7541

Svazek periodika

22

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

16

Strana od-do

1553-1568

Kód UT WoS článku

000663568900013

EID výsledku v databázi Scopus

2-s2.0-85110385548