Preliminary report on the use of the satellite data and data fusion techniques, based on the historical data

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00020699%3A_____%2F17%3AN0000112" target="_blank" >RIV/00020699:_____/17:N0000112 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Preliminary report on the use of the satellite data and data fusion techniques, based on the historical data

Original language description

In the scope of the SAMIRA project, an important task is a development of data fusion methods combining in situ measurements, satellite observations and chemical transport modelling (CTM) outputs. These three sources are mutually complimentary – in situ measurements provide accurate actual levels of concentrations, satellite data provide reasonably continuous observations of spatial patterns and modelling outputs provide spatially continuous coverage of given area. None of these datasets on its own is ideal for mapping purposes due to either substantial data gaps, insufficient spatial resolution, or large uncertainties. For the combination of different data sources, a range of various methods can be used to create spatial concentration fields. Such methods are often referred to as 'data assimilation' and 'data fusion'. 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 SAMIRA project, the residual kriging applied separately for the rural and urban background areas with the subsequent merging of these maps layers by population density is used, i.e. the regression – interpolation – merging mapping. It combines in-situ measurements, chemistry transport model results, and Earth Observation (satellite). In this paper, NO2, SO2, PM10, and PM2.5 data for 2014 are examined, at two spatial domains. In a use of the satellite data, the major obstacle is a frequent occurrence of spatial gaps. Trying to overcome this obstacle, gapfilling has been selected and applied.

Czech name

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Czech description

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Type

O - Miscellaneous

CEP classification

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OECD FORD branch

10509 - Meteorology and atmospheric sciences

Project

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Continuities

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Publication year

2017

Confidentiality

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