Filtering properties of wavelets for local background‐error correlations
Background‐error covariances can be estimated from an ensemble of forecast differences. The finite size of the ensemble induces a sampling noise in the calculated statistics. It is shown formally that a wavelet diagonal approach amounts to locally averaging the correlations, and its ability to spati...
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| Veröffentlicht in: | Quarterly journal of the Royal Meteorological Society 2007-01, Vol.133 (623), p.363-379 |
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| container_title | Quarterly journal of the Royal Meteorological Society |
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| creator | Pannekoucke, Olivier Berre, Loïk Desroziers, Gerald |
| description | Background‐error covariances can be estimated from an ensemble of forecast differences. The finite size of the ensemble induces a sampling noise in the calculated statistics. It is shown formally that a wavelet diagonal approach amounts to locally averaging the correlations, and its ability to spatially filter this sampling noise is thus investigated experimentally.
This is first studied in a simple analytical one‐dimensional framework. The capacity of a wavelet diagonal approach to model the scale variations over the domain is illustrated. Moreover, the sampling noise appears to be better filtered than when only using a Schur filter, in particular for small ensembles.
The filtering properties are then illustrated for an ensemble of Météo‐France Arpège forecasts. This is done both for the ‘time‐averaged correlations’, and for the ‘correlations of the day’. It is shown that the wavelets are able to extract some length‐scale variations that are related to the meteorological situation. Copyright © 2007 Royal Meteorological Society |
| doi_str_mv | 10.1002/qj.33 |
| format | Article |
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This is first studied in a simple analytical one‐dimensional framework. The capacity of a wavelet diagonal approach to model the scale variations over the domain is illustrated. Moreover, the sampling noise appears to be better filtered than when only using a Schur filter, in particular for small ensembles.
The filtering properties are then illustrated for an ensemble of Météo‐France Arpège forecasts. This is done both for the ‘time‐averaged correlations’, and for the ‘correlations of the day’. It is shown that the wavelets are able to extract some length‐scale variations that are related to the meteorological situation. Copyright © 2007 Royal Meteorological Society</description><identifier>ISSN: 0035-9009</identifier><identifier>EISSN: 1477-870X</identifier><identifier>DOI: 10.1002/qj.33</identifier><identifier>CODEN: QJRMAM</identifier><language>eng</language><publisher>Chichester, UK: John Wiley & Sons, Ltd</publisher><subject>assimilation ensemble ; background‐error covariance ; Earth, ocean, space ; Ensemble Kalman Filter ; Exact sciences and technology ; External geophysics ; Meteorology ; Other ; Physics of the high neutral atmosphere ; sampling noise ; Sciences of the Universe ; spherical wavelet ; wavelet on the circle</subject><ispartof>Quarterly journal of the Royal Meteorological Society, 2007-01, Vol.133 (623), p.363-379</ispartof><rights>Copyright © 2007 Royal Meteorological Society</rights><rights>2007 INIST-CNRS</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3813-90a3652317d65d034f35a19ad0e6241ff6bd4ed29bd33c57fb7e00e275ae185a3</citedby><cites>FETCH-LOGICAL-c3813-90a3652317d65d034f35a19ad0e6241ff6bd4ed29bd33c57fb7e00e275ae185a3</cites><orcidid>0000-0002-3093-7668 ; 0000-0002-3249-2818</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%2Fqj.33$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fqj.33$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>230,314,776,780,881,1411,4010,27900,27901,27902,45550,45551</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=18635690$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://meteofrance.hal.science/meteo-00202094$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Pannekoucke, Olivier</creatorcontrib><creatorcontrib>Berre, Loïk</creatorcontrib><creatorcontrib>Desroziers, Gerald</creatorcontrib><title>Filtering properties of wavelets for local background‐error correlations</title><title>Quarterly journal of the Royal Meteorological Society</title><description>Background‐error covariances can be estimated from an ensemble of forecast differences. The finite size of the ensemble induces a sampling noise in the calculated statistics. It is shown formally that a wavelet diagonal approach amounts to locally averaging the correlations, and its ability to spatially filter this sampling noise is thus investigated experimentally.
This is first studied in a simple analytical one‐dimensional framework. The capacity of a wavelet diagonal approach to model the scale variations over the domain is illustrated. Moreover, the sampling noise appears to be better filtered than when only using a Schur filter, in particular for small ensembles.
The filtering properties are then illustrated for an ensemble of Météo‐France Arpège forecasts. This is done both for the ‘time‐averaged correlations’, and for the ‘correlations of the day’. It is shown that the wavelets are able to extract some length‐scale variations that are related to the meteorological situation. Copyright © 2007 Royal Meteorological Society</description><subject>assimilation ensemble</subject><subject>background‐error covariance</subject><subject>Earth, ocean, space</subject><subject>Ensemble Kalman Filter</subject><subject>Exact sciences and technology</subject><subject>External geophysics</subject><subject>Meteorology</subject><subject>Other</subject><subject>Physics of the high neutral atmosphere</subject><subject>sampling noise</subject><subject>Sciences of the Universe</subject><subject>spherical wavelet</subject><subject>wavelet on the circle</subject><issn>0035-9009</issn><issn>1477-870X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2007</creationdate><recordtype>article</recordtype><recordid>eNpt0M1KAzEQB_AgCtaPd9iLepDWyc5m0z2KWD8oiKDgLaS7E01NN22yrXjzEXxGn8RIRT1IDnPIjz8zf8b2OQw4QH6ymA4QN1iPF1L2hxIeNlkPAEW_Aqi22U6MUwAQMpc9dj2yrqNg28dsHvycQmcpZt5kL3pFjrqYGR8y52vtsomunx-DX7bNx9s7hZA-ah8COd1Z38Y9tmW0i7T_PXfZ_ej87uyyP765uDo7HfdrHHJMS2gsRY5cNqVoAAuDQvNKN0BlXnBjyklTUJNXkwaxFtJMJAFQLoUmPhQad9nxOvdJOzUPdqbDq_LaqsvTsZpRR16lFtKrihVP-nCt03mLJcVOzWysyTndkl9GlQOWBeaY4MEa1sHHGMj8ZHNQX72qxVThHzfXMbVigm5rG3_xsERRVpDc0dq9WEev_4ep2-uU-AmYOYTo</recordid><startdate>200701</startdate><enddate>200701</enddate><creator>Pannekoucke, Olivier</creator><creator>Berre, Loïk</creator><creator>Desroziers, Gerald</creator><general>John Wiley & Sons, Ltd</general><general>Wiley</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TG</scope><scope>F1W</scope><scope>H96</scope><scope>KL.</scope><scope>L.G</scope><scope>1XC</scope><scope>VOOES</scope><orcidid>https://orcid.org/0000-0002-3093-7668</orcidid><orcidid>https://orcid.org/0000-0002-3249-2818</orcidid></search><sort><creationdate>200701</creationdate><title>Filtering properties of wavelets for local background‐error correlations</title><author>Pannekoucke, Olivier ; Berre, Loïk ; Desroziers, Gerald</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3813-90a3652317d65d034f35a19ad0e6241ff6bd4ed29bd33c57fb7e00e275ae185a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2007</creationdate><topic>assimilation ensemble</topic><topic>background‐error covariance</topic><topic>Earth, ocean, space</topic><topic>Ensemble Kalman Filter</topic><topic>Exact sciences and technology</topic><topic>External geophysics</topic><topic>Meteorology</topic><topic>Other</topic><topic>Physics of the high neutral atmosphere</topic><topic>sampling noise</topic><topic>Sciences of the Universe</topic><topic>spherical wavelet</topic><topic>wavelet on the circle</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pannekoucke, Olivier</creatorcontrib><creatorcontrib>Berre, Loïk</creatorcontrib><creatorcontrib>Desroziers, Gerald</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Meteorological & Geoastrophysical 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><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><jtitle>Quarterly journal of the Royal Meteorological Society</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Pannekoucke, Olivier</au><au>Berre, Loïk</au><au>Desroziers, Gerald</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Filtering properties of wavelets for local background‐error correlations</atitle><jtitle>Quarterly journal of the Royal Meteorological Society</jtitle><date>2007-01</date><risdate>2007</risdate><volume>133</volume><issue>623</issue><spage>363</spage><epage>379</epage><pages>363-379</pages><issn>0035-9009</issn><eissn>1477-870X</eissn><coden>QJRMAM</coden><abstract>Background‐error covariances can be estimated from an ensemble of forecast differences. The finite size of the ensemble induces a sampling noise in the calculated statistics. It is shown formally that a wavelet diagonal approach amounts to locally averaging the correlations, and its ability to spatially filter this sampling noise is thus investigated experimentally.
This is first studied in a simple analytical one‐dimensional framework. The capacity of a wavelet diagonal approach to model the scale variations over the domain is illustrated. Moreover, the sampling noise appears to be better filtered than when only using a Schur filter, in particular for small ensembles.
The filtering properties are then illustrated for an ensemble of Météo‐France Arpège forecasts. This is done both for the ‘time‐averaged correlations’, and for the ‘correlations of the day’. It is shown that the wavelets are able to extract some length‐scale variations that are related to the meteorological situation. Copyright © 2007 Royal Meteorological Society</abstract><cop>Chichester, UK</cop><pub>John Wiley & Sons, Ltd</pub><doi>10.1002/qj.33</doi><tpages>17</tpages><orcidid>https://orcid.org/0000-0002-3093-7668</orcidid><orcidid>https://orcid.org/0000-0002-3249-2818</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | Quarterly journal of the Royal Meteorological Society, 2007-01, Vol.133 (623), p.363-379 |
| issn | 0035-9009 1477-870X |
| language | eng |
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| source | Wiley Online Library - AutoHoldings Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
| subjects | assimilation ensemble background‐error covariance Earth, ocean, space Ensemble Kalman Filter Exact sciences and technology External geophysics Meteorology Other Physics of the high neutral atmosphere sampling noise Sciences of the Universe spherical wavelet wavelet on the circle |
| title | Filtering properties of wavelets for local background‐error correlations |
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