Lexis weather and climate large-scale pilot

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F21%3A10246000" target="_blank" >RIV/61989100:27740/21:10246000 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007%2F978-3-030-50454-0_25" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-030-50454-0_25</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-50454-0_25" target="_blank" >10.1007/978-3-030-50454-0_25</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Lexis weather and climate large-scale pilot

Popis výsledku v původním jazyce

The LEXIS Weather and Climate Large-scale Pilot will deliver a system for prediction of water-food-energy nexus phenomena and their associated socio-economic impacts. The system will be based on multiple model layers chained together, namely global weather and climate models, high-resolution regional weather models, domain-specific application models (such as hydrological, forest fire risk forecasts), impact models providing information for key decision and policy makers (such as air quality, agriculture crop production, and extreme rainfall detection for flood mapping). This paper will report about the first results of this pilot in terms of serving model output data and products with Cloud and High Performance Data Analytics (HPDA) environments, on top a Weather Climate Data APIs (ECMWF), as well as the porting of models on the LEXIS Infrastructure via different virtualization strategies (virtual machine and containers). (C) Springer Nature Switzerland AG 2021.

Název v anglickém jazyce

Lexis weather and climate large-scale pilot

Popis výsledku anglicky

The LEXIS Weather and Climate Large-scale Pilot will deliver a system for prediction of water-food-energy nexus phenomena and their associated socio-economic impacts. The system will be based on multiple model layers chained together, namely global weather and climate models, high-resolution regional weather models, domain-specific application models (such as hydrological, forest fire risk forecasts), impact models providing information for key decision and policy makers (such as air quality, agriculture crop production, and extreme rainfall detection for flood mapping). This paper will report about the first results of this pilot in terms of serving model output data and products with Cloud and High Performance Data Analytics (HPDA) environments, on top a Weather Climate Data APIs (ECMWF), as well as the porting of models on the LEXIS Infrastructure via different virtualization strategies (virtual machine and containers). (C) Springer Nature Switzerland AG 2021.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

Advances in Intelligent Systems and Computing. Volume 1194

ISBN

978-3-030-50454-0

ISSN

2194-5357

e-ISSN

—

Počet stran výsledku

11

Strana od-do

267-277

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Lodž

Datum konání akce

1. 7. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—