Qualitative analysis of precipiation distribution in Poland with use of different data sources

Geographical Information Systems (GIS) can be used to integrate data from different sources and in different formats to perform innovative spatial and temporal analysis. GIS can be also applied for climatic research to manage, investigate and display all kinds of weather data. <br>&...

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Main Authors: J. Walawender, I. Dyras, B. Łapeta, D. Serafin-Rek, A. Twardowski
Format: Article
Language:English
Published: Copernicus Publications 2008-04-01
Series:Advances in Science and Research
Online Access:http://www.adv-sci-res.net/2/27/2008/asr-2-27-2008.pdf
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spelling doaj-886cc0fafd864c62a93b3be95d1528dc2020-11-24T22:29:56ZengCopernicus PublicationsAdvances in Science and Research1992-06281992-06362008-04-0122730Qualitative analysis of precipiation distribution in Poland with use of different data sourcesJ. WalawenderI. DyrasB. ŁapetaD. Serafin-RekA. TwardowskiGeographical Information Systems (GIS) can be used to integrate data from different sources and in different formats to perform innovative spatial and temporal analysis. GIS can be also applied for climatic research to manage, investigate and display all kinds of weather data. <br><br> The main objective of this study is to demonstrate that GIS is a useful tool to examine and visualise precipitation distribution obtained from different data sources: ground measurements, satellite and radar data. <br><br> Three selected days (30 cases) with convective rainfall situations were analysed. Firstly, scalable GRID-based approach was applied to store data from three different sources in comparable layout. Then, geoprocessing algorithm was created within ArcGIS 9.2 environment. The algorithm included: GRID definition, reclassification and raster algebra. All of the calculations and procedures were performed automatically. Finally, contingency tables and pie charts were created to show relationship between ground measurements and both satellite and radar derived data. The results were visualised on maps. http://www.adv-sci-res.net/2/27/2008/asr-2-27-2008.pdf
collection DOAJ
language English
format Article
sources DOAJ
author J. Walawender
I. Dyras
B. Łapeta
D. Serafin-Rek
A. Twardowski
spellingShingle J. Walawender
I. Dyras
B. Łapeta
D. Serafin-Rek
A. Twardowski
Qualitative analysis of precipiation distribution in Poland with use of different data sources
Advances in Science and Research
author_facet J. Walawender
I. Dyras
B. Łapeta
D. Serafin-Rek
A. Twardowski
author_sort J. Walawender
title Qualitative analysis of precipiation distribution in Poland with use of different data sources
title_short Qualitative analysis of precipiation distribution in Poland with use of different data sources
title_full Qualitative analysis of precipiation distribution in Poland with use of different data sources
title_fullStr Qualitative analysis of precipiation distribution in Poland with use of different data sources
title_full_unstemmed Qualitative analysis of precipiation distribution in Poland with use of different data sources
title_sort qualitative analysis of precipiation distribution in poland with use of different data sources
publisher Copernicus Publications
series Advances in Science and Research
issn 1992-0628
1992-0636
publishDate 2008-04-01
description Geographical Information Systems (GIS) can be used to integrate data from different sources and in different formats to perform innovative spatial and temporal analysis. GIS can be also applied for climatic research to manage, investigate and display all kinds of weather data. <br><br> The main objective of this study is to demonstrate that GIS is a useful tool to examine and visualise precipitation distribution obtained from different data sources: ground measurements, satellite and radar data. <br><br> Three selected days (30 cases) with convective rainfall situations were analysed. Firstly, scalable GRID-based approach was applied to store data from three different sources in comparable layout. Then, geoprocessing algorithm was created within ArcGIS 9.2 environment. The algorithm included: GRID definition, reclassification and raster algebra. All of the calculations and procedures were performed automatically. Finally, contingency tables and pie charts were created to show relationship between ground measurements and both satellite and radar derived data. The results were visualised on maps.
url http://www.adv-sci-res.net/2/27/2008/asr-2-27-2008.pdf
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