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Development of an ensemble Kalman filter data assimilation system for the Venusian atmosphere
The size and mass of Venus is similar to those of the Earth; however, its atmospheric dynamics are considerably different and they are poorly understood due to limited observations and computational difficulties. Here, we developed a data assimilation system based on the local ensemble transform Kal...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5571110/ https://www.ncbi.nlm.nih.gov/pubmed/28839201 http://dx.doi.org/10.1038/s41598-017-09461-1 |
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author | Sugimoto, Norihiko Yamazaki, Akira Kouyama, Toru Kashimura, Hiroki Enomoto, Takeshi Takagi, Masahiro |
author_facet | Sugimoto, Norihiko Yamazaki, Akira Kouyama, Toru Kashimura, Hiroki Enomoto, Takeshi Takagi, Masahiro |
author_sort | Sugimoto, Norihiko |
collection | PubMed |
description | The size and mass of Venus is similar to those of the Earth; however, its atmospheric dynamics are considerably different and they are poorly understood due to limited observations and computational difficulties. Here, we developed a data assimilation system based on the local ensemble transform Kalman filter (LETKF) for a Venusian Atmospheric GCM for the Earth Simulator (VAFES), to make full use of the observational data. To examine the validity of the system, two datasets were assimilated separately into the VAFES forecasts forced with solar heating that excludes the diurnal component Qz; one was created from a VAFES run forced with solar heating that includes the diurnal component Qt, whereas the other was based on observations made by the Venus Monitoring Camera (VMC) onboard the Venus Express. The VAFES-LETKF system rapidly reduced the errors between the analysis and forecasts. In addition, the VAFES-LETKF system successfully reproduced the thermal tide excited by the diurnal component of solar heating, even though the second datasets only included horizontal winds at a single altitude on the dayside with a long interval of approximately one Earth day. This advanced system could be useful in the analysis of future datasets from the Venus Climate Orbiter ‘Akatsuki’. |
format | Online Article Text |
id | pubmed-5571110 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-55711102017-09-01 Development of an ensemble Kalman filter data assimilation system for the Venusian atmosphere Sugimoto, Norihiko Yamazaki, Akira Kouyama, Toru Kashimura, Hiroki Enomoto, Takeshi Takagi, Masahiro Sci Rep Article The size and mass of Venus is similar to those of the Earth; however, its atmospheric dynamics are considerably different and they are poorly understood due to limited observations and computational difficulties. Here, we developed a data assimilation system based on the local ensemble transform Kalman filter (LETKF) for a Venusian Atmospheric GCM for the Earth Simulator (VAFES), to make full use of the observational data. To examine the validity of the system, two datasets were assimilated separately into the VAFES forecasts forced with solar heating that excludes the diurnal component Qz; one was created from a VAFES run forced with solar heating that includes the diurnal component Qt, whereas the other was based on observations made by the Venus Monitoring Camera (VMC) onboard the Venus Express. The VAFES-LETKF system rapidly reduced the errors between the analysis and forecasts. In addition, the VAFES-LETKF system successfully reproduced the thermal tide excited by the diurnal component of solar heating, even though the second datasets only included horizontal winds at a single altitude on the dayside with a long interval of approximately one Earth day. This advanced system could be useful in the analysis of future datasets from the Venus Climate Orbiter ‘Akatsuki’. Nature Publishing Group UK 2017-08-24 /pmc/articles/PMC5571110/ /pubmed/28839201 http://dx.doi.org/10.1038/s41598-017-09461-1 Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Sugimoto, Norihiko Yamazaki, Akira Kouyama, Toru Kashimura, Hiroki Enomoto, Takeshi Takagi, Masahiro Development of an ensemble Kalman filter data assimilation system for the Venusian atmosphere |
title | Development of an ensemble Kalman filter data assimilation system for the Venusian atmosphere |
title_full | Development of an ensemble Kalman filter data assimilation system for the Venusian atmosphere |
title_fullStr | Development of an ensemble Kalman filter data assimilation system for the Venusian atmosphere |
title_full_unstemmed | Development of an ensemble Kalman filter data assimilation system for the Venusian atmosphere |
title_short | Development of an ensemble Kalman filter data assimilation system for the Venusian atmosphere |
title_sort | development of an ensemble kalman filter data assimilation system for the venusian atmosphere |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5571110/ https://www.ncbi.nlm.nih.gov/pubmed/28839201 http://dx.doi.org/10.1038/s41598-017-09461-1 |
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