| Item type |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2015-03-26 |
| タイトル |
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タイトル |
Data Assimilation of the High-Resolution Sea Surface Temperature Obtained from the Aqua-Terra Satellites (MODIS-SST) Using an Ensemble Kalman Filter |
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言語 |
en |
| 言語 |
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言語 |
eng |
| キーワード |
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言語 |
en |
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主題Scheme |
Other |
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主題 |
Moderate Resolution Imaging Spectroradiometer (MODIS) sensors; ocean circulation model; vertical projection; Ensemble Kalman Filter; dynamic quality control |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
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資源タイプ |
journal article |
| 著者 |
Miyazawa, Yasumasa
村上, 浩
Miyama, Toru
Varlamov, Sergey M.
Guo, Xinyu
Waseda, Takuji
Sil, Sourav
Miyazawa, Yasumasa
Murakami, Hiroshi
Miyama, Toru
Varlamov, Sergey M.
Guo, Xinyu
Waseda, Takuji
Sil, Sourav
|
| 著者所属 |
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海洋研究開発機構(JAMSTEC) |
| 著者所属 |
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宇宙航空研究開発機構(JAXA) |
| 著者所属 |
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海洋研究開発機構(JAMSTEC) |
| 著者所属 |
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海洋研究開発機構(JAMSTEC) |
| 著者所属 |
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海洋研究開発機構(JAMSTEC) |
| 著者所属 |
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海洋研究開発機構(JAMSTEC) |
| 著者所属 |
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海洋研究開発機構(JAMSTEC) |
| 著者所属（英） |
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en |
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Japan Agency for Marine-Earth Science and Technology(JAMSTEC) |
| 著者所属（英） |
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en |
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Japan Aerospace Exploration Agency(JAXA) |
| 著者所属（英） |
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en |
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Japan Agency for Marine-Earth Science and Technology(JAMSTEC) |
| 著者所属（英） |
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en |
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Japan Agency for Marine-Earth Science and Technology(JAMSTEC) |
| 著者所属（英） |
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en |
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Japan Agency for Marine-Earth Science and Technology(JAMSTEC) |
| 著者所属（英） |
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en |
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Japan Agency for Marine-Earth Science and Technology(JAMSTEC) |
| 著者所属（英） |
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en |
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Japan Agency for Marine-Earth Science and Technology(JAMSTEC) |
| 出版者（英） |
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出版者 |
MDPI |
| 書誌情報 |
en : Remote Sensing
巻 5,
号 6,
p. 3123-3139,
発行日 2013-06
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| 抄録（英） |
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内容記述タイプ |
Other |
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内容記述 |
We develop an assimilation method of high horizontal resolution sea surface temperature data, provided from the Moderate Resolution Imaging Spectroradiometer (MODIS-SST) sensors boarded on the Aqua and Terra satellites operated by National Aeronautics and Space Administration (NASA), focusing on the reproducibility of the Kuroshio front variations south of Japan in February 2010. Major concerns associated with the development are (1) negative temperature bias due to the cloud effects, and (2) the representation of error covariance for detection of highly variable phenomena. We treat them by utilizing an advanced data assimilation method allowing use of spatiotemporally varying error covariance: the Local Ensemble Transformation Kalman Filter (LETKF). It is found that the quality control, by comparing the model forecast variable with the MODIS-SST data, is useful to remove the negative temperature bias and results in the mean negative bias within -0.4 °C. The additional assimilation of MODIS-SST enhances spatial variability of analysis SST over 50 km to 25 km scales. The ensemble spread variance is effectively utilized for excluding the erroneous temperature data from the assimilation process. |
| ISSN |
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収録物識別子タイプ |
ISSN |
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収録物識別子 |
2072-4292 |
| DOI |
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関連タイプ |
isIdenticalTo |
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識別子タイプ |
DOI |
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関連識別子 |
http://dx.doi.org/10.3390/rs5063123 |
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関連名称 |
info:doi/10.3390/rs5063123 |
| 権利 |
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権利情報 |
(C) 2013 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/). |
| 著者版フラグ |
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出版タイプ |
VoR |
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出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| 資料番号 |
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内容記述タイプ |
Other |
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内容記述 |
資料番号: PA1410065000 |
PA1410065.pdf (2.5 MB)
