Dynamics of meteorological time series on the base of ground measurements and retrospective data from MERRA‐2 for Poland
A comparison study has been performed to assess the dynamics of meteorological processes in Poland on the basis of meteorological time series of air pressure, air temperature and wind speed coming from 35 synoptic stations belonging to the Institute of Meteorology and Water Management—National Resea...
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description | A comparison study has been performed to assess the dynamics of meteorological processes in Poland on the basis of meteorological time series of air pressure, air temperature and wind speed coming from 35 synoptic stations belonging to the Institute of Meteorology and Water Management—National Research Institute (IMGW‐PIB) and from the nearest grid points of the Modern‐Era Retrospective Analysis for Research and Applications, Version 2 (MERRA‐2) produced at NASA's Global Modelling and Assimilation Office from January 1, 2007 to October 31, 2016. Apart from comparative statistics, the differences in the multifractal properties of the time series were evaluated with the use of MultiFractal Detrended Fluctuation Analysis (MFDFA), both for hourly and daily data, showing a high degree of similarity between the MERRA‐2 and IMGW‐PIB series. For the air pressure and air temperature, not only were high determination coefficients (close to .99) between the time series coming from the two sources noticed, but there were also similarities with the MFDFA parameters. Lower correlations between the time series of the wind speed obtained from the two studied databases were observed, which was related to differences in the data structure and methodology of the measurements for specific IMGW‐PIB stations. Additionally, to verify data similarities coming from the IMGW‐PIB and MERRA‐2 databases, the correlations between specific multifractal parameters and the orography were estimated and compared. For the air pressure and temperature, a remarkably high correlation was found between the multifractal parameter α0 and the height above sea level of the measurement site. An analysis of the source of multifractality was performed, indicating that, for all studied meteorological elements and both data sources, the long‐range correlations prevail.
A comparison has been performed for Poland using meteorological time series from 35 synoptic stations of the Institute of Meteorology and Water Management (IMGW‐PIB) and the nearest grid points of the Modern‐Era Retrospective Analysis for Research and Applications (MERRA‐2). High similarity of time series coming from those two data sources confirmed by statistical and multifractal analyses suggest that MERRA‐2 data can be very useful for analyses of the climate dynamics change and can be used interchangeably with the ground data. |
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A comparison has been performed for Poland using meteorological time series from 35 synoptic stations of the Institute of Meteorology and Water Management (IMGW‐PIB) and the nearest grid points of the Modern‐Era Retrospective Analysis for Research and Applications (MERRA‐2). High similarity of time series coming from those two data sources confirmed by statistical and multifractal analyses suggest that MERRA‐2 data can be very useful for analyses of the climate dynamics change and can be used interchangeably with the ground data.</description><identifier>ISSN: 0899-8418</identifier><identifier>EISSN: 1097-0088</identifier><identifier>DOI: 10.1002/joc.6787</identifier><language>eng</language><publisher>Chichester, UK: John Wiley & Sons, Ltd</publisher><subject>Air temperature ; Coefficients ; Correlation ; Data ; Data structures ; Dynamics ; IMGW‐PIB ; MERRA‐2 ; meteorological elements ; Meteorology ; multifractal analysis ; Orography ; Parameter estimation ; Parameters ; Pressure ; Sea level ; Sea level measurements ; Similarity ; Statistical analysis ; Statistical methods ; Time series ; Water management ; Wind ; Wind speed</subject><ispartof>International journal of climatology, 2021-01, Vol.41 (S1), p.E1531-E1552</ispartof><rights>2020 The Authors published by John Wiley & Sons Ltd on behalf of Royal Meteorological Society.</rights><rights>2020. This article is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2427-2690050c44882443fdc85dd9664462e43a2f4fb65cb477df66dabced5db32e4a3</citedby><cites>FETCH-LOGICAL-c2427-2690050c44882443fdc85dd9664462e43a2f4fb65cb477df66dabced5db32e4a3</cites><orcidid>0000-0002-9235-8119 ; 0000-0003-0979-177X ; 0000-0003-1953-7142 ; 0000-0002-6876-0837</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fjoc.6787$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjoc.6787$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,45574,45575</link.rule.ids></links><search><creatorcontrib>Gos, Magdalena</creatorcontrib><creatorcontrib>Baranowski, Piotr</creatorcontrib><creatorcontrib>Krzyszczak, Jaromir</creatorcontrib><creatorcontrib>Kieliszek, Adam</creatorcontrib><creatorcontrib>Siwek, Krzysztof</creatorcontrib><title>Dynamics of meteorological time series on the base of ground measurements and retrospective data from MERRA‐2 for Poland</title><title>International journal of climatology</title><description>A comparison study has been performed to assess the dynamics of meteorological processes in Poland on the basis of meteorological time series of air pressure, air temperature and wind speed coming from 35 synoptic stations belonging to the Institute of Meteorology and Water Management—National Research Institute (IMGW‐PIB) and from the nearest grid points of the Modern‐Era Retrospective Analysis for Research and Applications, Version 2 (MERRA‐2) produced at NASA's Global Modelling and Assimilation Office from January 1, 2007 to October 31, 2016. Apart from comparative statistics, the differences in the multifractal properties of the time series were evaluated with the use of MultiFractal Detrended Fluctuation Analysis (MFDFA), both for hourly and daily data, showing a high degree of similarity between the MERRA‐2 and IMGW‐PIB series. For the air pressure and air temperature, not only were high determination coefficients (close to .99) between the time series coming from the two sources noticed, but there were also similarities with the MFDFA parameters. Lower correlations between the time series of the wind speed obtained from the two studied databases were observed, which was related to differences in the data structure and methodology of the measurements for specific IMGW‐PIB stations. Additionally, to verify data similarities coming from the IMGW‐PIB and MERRA‐2 databases, the correlations between specific multifractal parameters and the orography were estimated and compared. For the air pressure and temperature, a remarkably high correlation was found between the multifractal parameter α0 and the height above sea level of the measurement site. An analysis of the source of multifractality was performed, indicating that, for all studied meteorological elements and both data sources, the long‐range correlations prevail.
A comparison has been performed for Poland using meteorological time series from 35 synoptic stations of the Institute of Meteorology and Water Management (IMGW‐PIB) and the nearest grid points of the Modern‐Era Retrospective Analysis for Research and Applications (MERRA‐2). High similarity of time series coming from those two data sources confirmed by statistical and multifractal analyses suggest that MERRA‐2 data can be very useful for analyses of the climate dynamics change and can be used interchangeably with the ground data.</description><subject>Air temperature</subject><subject>Coefficients</subject><subject>Correlation</subject><subject>Data</subject><subject>Data structures</subject><subject>Dynamics</subject><subject>IMGW‐PIB</subject><subject>MERRA‐2</subject><subject>meteorological elements</subject><subject>Meteorology</subject><subject>multifractal analysis</subject><subject>Orography</subject><subject>Parameter estimation</subject><subject>Parameters</subject><subject>Pressure</subject><subject>Sea level</subject><subject>Sea level measurements</subject><subject>Similarity</subject><subject>Statistical analysis</subject><subject>Statistical methods</subject><subject>Time series</subject><subject>Water management</subject><subject>Wind</subject><subject>Wind speed</subject><issn>0899-8418</issn><issn>1097-0088</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</sourceid><recordid>eNp10M1OwzAMAOAIgcQYSDxCJC5cOtIsTZPjNP41NDTBuUoTZ3Rqm5G0oHHiEXhGnoSMceVkyf5sy0boNCWjlBB6sXJ6xHOR76FBSmSeECLEPhoQIWUiWCoO0VEIK0KIlCkfoI_LTauaSgfsLG6gA-dd7ZaVVjXuqgZwAF9BrLa4ewFcqgBbufSub01sUKH30EDbBaxiwkPnXViD7qo3wEZ1ClvvGvxwtVhMvj-_KLbO40dXR3yMDqyqA5z8xSF6vr56mt4ms_nN3XQySzRlNE8ol4RkRDMmBGVsbI0WmTGSc8Y4BTZW1DJb8kyXLM-N5dyoUoPJTDmOZTUeorPd3LV3rz2Erli53rdxZUFZLjLJM0aiOt8pHQ8IHmyx9lWj_KZISbH9bOzSxfazkSY7-l7VsPnXFffz6a__AX4Me-Y</recordid><startdate>202101</startdate><enddate>202101</enddate><creator>Gos, Magdalena</creator><creator>Baranowski, Piotr</creator><creator>Krzyszczak, Jaromir</creator><creator>Kieliszek, Adam</creator><creator>Siwek, Krzysztof</creator><general>John Wiley & Sons, Ltd</general><general>Wiley Subscription Services, Inc</general><scope>24P</scope><scope>WIN</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TG</scope><scope>7TN</scope><scope>F1W</scope><scope>H96</scope><scope>KL.</scope><scope>L.G</scope><orcidid>https://orcid.org/0000-0002-9235-8119</orcidid><orcidid>https://orcid.org/0000-0003-0979-177X</orcidid><orcidid>https://orcid.org/0000-0003-1953-7142</orcidid><orcidid>https://orcid.org/0000-0002-6876-0837</orcidid></search><sort><creationdate>202101</creationdate><title>Dynamics of meteorological time series on the base of ground measurements and retrospective data from MERRA‐2 for Poland</title><author>Gos, Magdalena ; Baranowski, Piotr ; Krzyszczak, Jaromir ; Kieliszek, Adam ; Siwek, Krzysztof</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2427-2690050c44882443fdc85dd9664462e43a2f4fb65cb477df66dabced5db32e4a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Air temperature</topic><topic>Coefficients</topic><topic>Correlation</topic><topic>Data</topic><topic>Data structures</topic><topic>Dynamics</topic><topic>IMGW‐PIB</topic><topic>MERRA‐2</topic><topic>meteorological elements</topic><topic>Meteorology</topic><topic>multifractal analysis</topic><topic>Orography</topic><topic>Parameter estimation</topic><topic>Parameters</topic><topic>Pressure</topic><topic>Sea level</topic><topic>Sea level measurements</topic><topic>Similarity</topic><topic>Statistical analysis</topic><topic>Statistical methods</topic><topic>Time series</topic><topic>Water management</topic><topic>Wind</topic><topic>Wind speed</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gos, Magdalena</creatorcontrib><creatorcontrib>Baranowski, Piotr</creatorcontrib><creatorcontrib>Krzyszczak, Jaromir</creatorcontrib><creatorcontrib>Kieliszek, Adam</creatorcontrib><creatorcontrib>Siwek, Krzysztof</creatorcontrib><collection>Wiley Online Library Open Access</collection><collection>Wiley Online Library Free Content</collection><collection>CrossRef</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Oceanic Abstracts</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><jtitle>International journal of climatology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gos, Magdalena</au><au>Baranowski, Piotr</au><au>Krzyszczak, Jaromir</au><au>Kieliszek, Adam</au><au>Siwek, Krzysztof</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Dynamics of meteorological time series on the base of ground measurements and retrospective data from MERRA‐2 for Poland</atitle><jtitle>International journal of climatology</jtitle><date>2021-01</date><risdate>2021</risdate><volume>41</volume><issue>S1</issue><spage>E1531</spage><epage>E1552</epage><pages>E1531-E1552</pages><issn>0899-8418</issn><eissn>1097-0088</eissn><abstract>A comparison study has been performed to assess the dynamics of meteorological processes in Poland on the basis of meteorological time series of air pressure, air temperature and wind speed coming from 35 synoptic stations belonging to the Institute of Meteorology and Water Management—National Research Institute (IMGW‐PIB) and from the nearest grid points of the Modern‐Era Retrospective Analysis for Research and Applications, Version 2 (MERRA‐2) produced at NASA's Global Modelling and Assimilation Office from January 1, 2007 to October 31, 2016. Apart from comparative statistics, the differences in the multifractal properties of the time series were evaluated with the use of MultiFractal Detrended Fluctuation Analysis (MFDFA), both for hourly and daily data, showing a high degree of similarity between the MERRA‐2 and IMGW‐PIB series. For the air pressure and air temperature, not only were high determination coefficients (close to .99) between the time series coming from the two sources noticed, but there were also similarities with the MFDFA parameters. Lower correlations between the time series of the wind speed obtained from the two studied databases were observed, which was related to differences in the data structure and methodology of the measurements for specific IMGW‐PIB stations. Additionally, to verify data similarities coming from the IMGW‐PIB and MERRA‐2 databases, the correlations between specific multifractal parameters and the orography were estimated and compared. For the air pressure and temperature, a remarkably high correlation was found between the multifractal parameter α0 and the height above sea level of the measurement site. An analysis of the source of multifractality was performed, indicating that, for all studied meteorological elements and both data sources, the long‐range correlations prevail.
A comparison has been performed for Poland using meteorological time series from 35 synoptic stations of the Institute of Meteorology and Water Management (IMGW‐PIB) and the nearest grid points of the Modern‐Era Retrospective Analysis for Research and Applications (MERRA‐2). High similarity of time series coming from those two data sources confirmed by statistical and multifractal analyses suggest that MERRA‐2 data can be very useful for analyses of the climate dynamics change and can be used interchangeably with the ground data.</abstract><cop>Chichester, UK</cop><pub>John Wiley & Sons, Ltd</pub><doi>10.1002/joc.6787</doi><tpages>22</tpages><orcidid>https://orcid.org/0000-0002-9235-8119</orcidid><orcidid>https://orcid.org/0000-0003-0979-177X</orcidid><orcidid>https://orcid.org/0000-0003-1953-7142</orcidid><orcidid>https://orcid.org/0000-0002-6876-0837</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Air temperature Coefficients Correlation Data Data structures Dynamics IMGW‐PIB MERRA‐2 meteorological elements Meteorology multifractal analysis Orography Parameter estimation Parameters Pressure Sea level Sea level measurements Similarity Statistical analysis Statistical methods Time series Water management Wind Wind speed |
title | Dynamics of meteorological time series on the base of ground measurements and retrospective data from MERRA‐2 for Poland |
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