Algoritm Theoretical Basis Document – Data fusion

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00020699%3A_____%2F17%3AN0000152" target="_blank" >RIV/00020699:_____/17:N0000152 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Algoritm Theoretical Basis Document – Data fusion

Popis výsledku v původním jazyce

This document provides a theoretical description of the data fusion methodology and its uncertainty estimates used within the SAMIRA project https://samira.nilu.no/, which aims to improve air quality assessment through synergetic use of data from satellites, in situ air quality monitoring networks and output from chemical transport models. Data fusion is a technique for combining two or more datasets in an objective, mathematically meaningful way, in order to obtain a new dataset which has superior properties to any of the input datasets. Data fusion is a subset of data assimilation methods in a wider sense. One of the often used data fusion methods is residual kriging. In the residual kriging, monitoring, modelling and other supplementary data are combined in multiple linear regression and subsequent spatial interpolation of its residuals by ordinary kriging (residual kriging).

Název v anglickém jazyce

Algoritm Theoretical Basis Document – Data fusion

Popis výsledku anglicky

This document provides a theoretical description of the data fusion methodology and its uncertainty estimates used within the SAMIRA project https://samira.nilu.no/, which aims to improve air quality assessment through synergetic use of data from satellites, in situ air quality monitoring networks and output from chemical transport models. Data fusion is a technique for combining two or more datasets in an objective, mathematically meaningful way, in order to obtain a new dataset which has superior properties to any of the input datasets. Data fusion is a subset of data assimilation methods in a wider sense. One of the often used data fusion methods is residual kriging. In the residual kriging, monitoring, modelling and other supplementary data are combined in multiple linear regression and subsequent spatial interpolation of its residuals by ordinary kriging (residual kriging).

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

10509 - Meteorology and atmospheric sciences

Projekt

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Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2017

Kód důvěrnosti údajů

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