Assimilation of Sparse Continuous Near‐Earth Weather Measurements by NECTAR Model Morphing
Non‐linear Error Compensation Technique with Associative Restoration (NECTAR) is a novel approach to the assimilation of fragmentary sensor data to produce a global nowcast of the near‐Earth space weather. NECTAR restores missing information by iteratively transforming (“morphing”) an underlying glo...
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description | Non‐linear Error Compensation Technique with Associative Restoration (NECTAR) is a novel approach to the assimilation of fragmentary sensor data to produce a global nowcast of the near‐Earth space weather. NECTAR restores missing information by iteratively transforming (“morphing”) an underlying global climatology model into agreement with currently available sensor data. The morphing procedure benefits from analysis of the inherent multiscale diurnal periodicity of the geosystems by processing 24‐hr time histories of the differences between measured and climate‐expected values at each sensor site. The 24‐hr deviation time series are used to compute and then globally interpolate the diurnal deviation harmonics. NECTAR therefore views the geosystem in terms of its periodic planetary‐scale basis to associate observed fragments of the activity with the grand‐scale weather processes of the matching variability scales. Such approach strengthens the restorative capability of the assimilation, specifically when only a limited number of observatories is available for the weather nowcast. Scenarios where the NECTAR concept works best are common in planetary‐scale near‐Earth weather applications, especially where sensor instrumentation is complex, expensive, and therefore scarce. To conduct the assimilation process, NECTAR employs a Hopfield feedback recurrent neural network commonly used in the associative memory architectures. Associative memories mimic human capability to restore full information from its initial fragments. When applied to the sparse spatial data, such a neural network becomes a nonlinear multiscale interpolator of missing information. Early tests of the NECTAR morphing reveal its enhanced capability to predict system dynamics over no‐data regions (spatial interpolation).
Key Points
A novel technique is presented for assimilating fragmentary sensor data to produce a global‐scale space weather nowcast
The technique iteratively transforms (“morphs”) an underlying global climatology model into agreement with available sensor data
The technique senses the inherent multiscale diurnal periodicity of geosystems to restore missing information over no‐data regions |
doi_str_mv | 10.1029/2020SW002463 |
format | Article |
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Key Points
A novel technique is presented for assimilating fragmentary sensor data to produce a global‐scale space weather nowcast
The technique iteratively transforms (“morphs”) an underlying global climatology model into agreement with available sensor data
The technique senses the inherent multiscale diurnal periodicity of geosystems to restore missing information over no‐data regions</description><identifier>ISSN: 1542-7390</identifier><identifier>ISSN: 1539-4964</identifier><identifier>EISSN: 1542-7390</identifier><identifier>DOI: 10.1029/2020SW002463</identifier><language>eng</language><publisher>Washington: John Wiley & Sons, Inc</publisher><subject>Assimilation ; Associative memory ; Climate models ; Climatology ; Computer architecture ; data assimilation ; Deviation ; diurnal harmonic analysis ; Earth ; Error compensation ; Fragments ; Global climate ; Hopfield networks ; Instrumentation ; Interpolation ; model morphing ; Morphing ; Neural networks ; Observatories ; Periodic variations ; Recurrent neural networks ; Sensors ; Space weather ; Spatial data ; spatial prediction ; System dynamics ; weather nowcast</subject><ispartof>Space Weather, 2020-11, Vol.18 (11), p.n/a</ispartof><rights>2020. The Authors.</rights><rights>2020. This article is published under http://creativecommons.org/licenses/by-nc/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-c3442-ceb61769e135fd19f7c544381f452b5bf907393ee38b76dad988b4ed38a47b593</citedby><cites>FETCH-LOGICAL-c3442-ceb61769e135fd19f7c544381f452b5bf907393ee38b76dad988b4ed38a47b593</cites><orcidid>0000-0002-9716-7688 ; 0000-0002-2158-6405 ; 0000-0002-7286-8509 ; 0000-0001-6551-2929 ; 0000-0002-3789-6299</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1029%2F2020SW002463$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1029%2F2020SW002463$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,1417,11562,27924,27925,45574,45575,46052,46476</link.rule.ids></links><search><creatorcontrib>Galkin, I. A.</creatorcontrib><creatorcontrib>Reinisch, B. W.</creatorcontrib><creatorcontrib>Vesnin, A. M.</creatorcontrib><creatorcontrib>Bilitza, D.</creatorcontrib><creatorcontrib>Fridman, S.</creatorcontrib><creatorcontrib>Habarulema, J. B.</creatorcontrib><creatorcontrib>Veliz, O.</creatorcontrib><title>Assimilation of Sparse Continuous Near‐Earth Weather Measurements by NECTAR Model Morphing</title><title>Space Weather</title><description>Non‐linear Error Compensation Technique with Associative Restoration (NECTAR) is a novel approach to the assimilation of fragmentary sensor data to produce a global nowcast of the near‐Earth space weather. NECTAR restores missing information by iteratively transforming (“morphing”) an underlying global climatology model into agreement with currently available sensor data. The morphing procedure benefits from analysis of the inherent multiscale diurnal periodicity of the geosystems by processing 24‐hr time histories of the differences between measured and climate‐expected values at each sensor site. The 24‐hr deviation time series are used to compute and then globally interpolate the diurnal deviation harmonics. NECTAR therefore views the geosystem in terms of its periodic planetary‐scale basis to associate observed fragments of the activity with the grand‐scale weather processes of the matching variability scales. Such approach strengthens the restorative capability of the assimilation, specifically when only a limited number of observatories is available for the weather nowcast. Scenarios where the NECTAR concept works best are common in planetary‐scale near‐Earth weather applications, especially where sensor instrumentation is complex, expensive, and therefore scarce. To conduct the assimilation process, NECTAR employs a Hopfield feedback recurrent neural network commonly used in the associative memory architectures. Associative memories mimic human capability to restore full information from its initial fragments. When applied to the sparse spatial data, such a neural network becomes a nonlinear multiscale interpolator of missing information. Early tests of the NECTAR morphing reveal its enhanced capability to predict system dynamics over no‐data regions (spatial interpolation).
Key Points
A novel technique is presented for assimilating fragmentary sensor data to produce a global‐scale space weather nowcast
The technique iteratively transforms (“morphs”) an underlying global climatology model into agreement with available sensor data
The technique senses the inherent multiscale diurnal periodicity of geosystems to restore missing information over no‐data regions</description><subject>Assimilation</subject><subject>Associative memory</subject><subject>Climate models</subject><subject>Climatology</subject><subject>Computer architecture</subject><subject>data assimilation</subject><subject>Deviation</subject><subject>diurnal harmonic analysis</subject><subject>Earth</subject><subject>Error compensation</subject><subject>Fragments</subject><subject>Global climate</subject><subject>Hopfield networks</subject><subject>Instrumentation</subject><subject>Interpolation</subject><subject>model morphing</subject><subject>Morphing</subject><subject>Neural networks</subject><subject>Observatories</subject><subject>Periodic variations</subject><subject>Recurrent neural networks</subject><subject>Sensors</subject><subject>Space weather</subject><subject>Spatial data</subject><subject>spatial prediction</subject><subject>System dynamics</subject><subject>weather nowcast</subject><issn>1542-7390</issn><issn>1539-4964</issn><issn>1542-7390</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</sourceid><recordid>eNp9kM1Kw0AURgdRsFZ3PsCAW6vzm8ksS4hVaCvYSjdCmCQ3NiXNxJkE6c5H8Bl9ElPqois3997F4bsfB6FrSu4oYfqeEUYWK0KYCPgJGlAp2EhxTU6P7nN04f1mz0gmBuht7H25LSvTlrbGtsCLxjgPOLJ1W9ad7Tyeg3E_X9-xce0ar8C0a3B4BsZ3DrZQtx6nOzyPo-X4Bc9sDlU_XbMu6_dLdFaYysPV3x6i14d4GT2Ops-Tp2g8HWVc9LUySAOqAg2UyyKnulCZFIKHtOg7pjItNOmbcwAepirITa7DMBWQ89AIlUrNh-jmkNs4-9GBb5ON7Vzdv0z2KhRROiA9dXugMme9d1AkjSu3xu0SSpK9v-TYX4-zA_5ZVrD7l00Wq5hRIhn_BYiWcUo</recordid><startdate>202011</startdate><enddate>202011</enddate><creator>Galkin, I. A.</creator><creator>Reinisch, B. W.</creator><creator>Vesnin, A. M.</creator><creator>Bilitza, D.</creator><creator>Fridman, S.</creator><creator>Habarulema, J. B.</creator><creator>Veliz, O.</creator><general>John Wiley & Sons, Inc</general><scope>24P</scope><scope>WIN</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TG</scope><scope>8FD</scope><scope>H8D</scope><scope>KL.</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0002-9716-7688</orcidid><orcidid>https://orcid.org/0000-0002-2158-6405</orcidid><orcidid>https://orcid.org/0000-0002-7286-8509</orcidid><orcidid>https://orcid.org/0000-0001-6551-2929</orcidid><orcidid>https://orcid.org/0000-0002-3789-6299</orcidid></search><sort><creationdate>202011</creationdate><title>Assimilation of Sparse Continuous Near‐Earth Weather Measurements by NECTAR Model Morphing</title><author>Galkin, I. A. ; Reinisch, B. W. ; Vesnin, A. M. ; Bilitza, D. ; Fridman, S. ; Habarulema, J. B. ; Veliz, O.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3442-ceb61769e135fd19f7c544381f452b5bf907393ee38b76dad988b4ed38a47b593</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Assimilation</topic><topic>Associative memory</topic><topic>Climate models</topic><topic>Climatology</topic><topic>Computer architecture</topic><topic>data assimilation</topic><topic>Deviation</topic><topic>diurnal harmonic analysis</topic><topic>Earth</topic><topic>Error compensation</topic><topic>Fragments</topic><topic>Global climate</topic><topic>Hopfield networks</topic><topic>Instrumentation</topic><topic>Interpolation</topic><topic>model morphing</topic><topic>Morphing</topic><topic>Neural networks</topic><topic>Observatories</topic><topic>Periodic variations</topic><topic>Recurrent neural networks</topic><topic>Sensors</topic><topic>Space weather</topic><topic>Spatial data</topic><topic>spatial prediction</topic><topic>System dynamics</topic><topic>weather nowcast</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Galkin, I. A.</creatorcontrib><creatorcontrib>Reinisch, B. W.</creatorcontrib><creatorcontrib>Vesnin, A. M.</creatorcontrib><creatorcontrib>Bilitza, D.</creatorcontrib><creatorcontrib>Fridman, S.</creatorcontrib><creatorcontrib>Habarulema, J. B.</creatorcontrib><creatorcontrib>Veliz, O.</creatorcontrib><collection>Wiley Online Library (Open Access Collection)</collection><collection>Wiley Online Library (Open Access Collection)</collection><collection>CrossRef</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Space Weather</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Galkin, I. A.</au><au>Reinisch, B. W.</au><au>Vesnin, A. M.</au><au>Bilitza, D.</au><au>Fridman, S.</au><au>Habarulema, J. B.</au><au>Veliz, O.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Assimilation of Sparse Continuous Near‐Earth Weather Measurements by NECTAR Model Morphing</atitle><jtitle>Space Weather</jtitle><date>2020-11</date><risdate>2020</risdate><volume>18</volume><issue>11</issue><epage>n/a</epage><issn>1542-7390</issn><issn>1539-4964</issn><eissn>1542-7390</eissn><abstract>Non‐linear Error Compensation Technique with Associative Restoration (NECTAR) is a novel approach to the assimilation of fragmentary sensor data to produce a global nowcast of the near‐Earth space weather. NECTAR restores missing information by iteratively transforming (“morphing”) an underlying global climatology model into agreement with currently available sensor data. The morphing procedure benefits from analysis of the inherent multiscale diurnal periodicity of the geosystems by processing 24‐hr time histories of the differences between measured and climate‐expected values at each sensor site. The 24‐hr deviation time series are used to compute and then globally interpolate the diurnal deviation harmonics. NECTAR therefore views the geosystem in terms of its periodic planetary‐scale basis to associate observed fragments of the activity with the grand‐scale weather processes of the matching variability scales. Such approach strengthens the restorative capability of the assimilation, specifically when only a limited number of observatories is available for the weather nowcast. Scenarios where the NECTAR concept works best are common in planetary‐scale near‐Earth weather applications, especially where sensor instrumentation is complex, expensive, and therefore scarce. To conduct the assimilation process, NECTAR employs a Hopfield feedback recurrent neural network commonly used in the associative memory architectures. Associative memories mimic human capability to restore full information from its initial fragments. When applied to the sparse spatial data, such a neural network becomes a nonlinear multiscale interpolator of missing information. Early tests of the NECTAR morphing reveal its enhanced capability to predict system dynamics over no‐data regions (spatial interpolation).
Key Points
A novel technique is presented for assimilating fragmentary sensor data to produce a global‐scale space weather nowcast
The technique iteratively transforms (“morphs”) an underlying global climatology model into agreement with available sensor data
The technique senses the inherent multiscale diurnal periodicity of geosystems to restore missing information over no‐data regions</abstract><cop>Washington</cop><pub>John Wiley & Sons, Inc</pub><doi>10.1029/2020SW002463</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0002-9716-7688</orcidid><orcidid>https://orcid.org/0000-0002-2158-6405</orcidid><orcidid>https://orcid.org/0000-0002-7286-8509</orcidid><orcidid>https://orcid.org/0000-0001-6551-2929</orcidid><orcidid>https://orcid.org/0000-0002-3789-6299</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Assimilation Associative memory Climate models Climatology Computer architecture data assimilation Deviation diurnal harmonic analysis Earth Error compensation Fragments Global climate Hopfield networks Instrumentation Interpolation model morphing Morphing Neural networks Observatories Periodic variations Recurrent neural networks Sensors Space weather Spatial data spatial prediction System dynamics weather nowcast |
title | Assimilation of Sparse Continuous Near‐Earth Weather Measurements by NECTAR Model Morphing |
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