Data Assimilation of the High-Resolution Sea Surface Temperature Obtained from the Aqua-Terra Satellites (MODIS-SST) Using an Ensemble Kalman Filter

DOI Web Site 被引用文献8件
  • Yasumasa Miyazawa
    Japan Agency for Marine-Earth Science and Technology, Yokohama, Kanagawa 236-0001, Japan
  • Hiroshi Murakami
    Japan Aerospace Exploration Agency, Tsukuba, Ibaragi 305-8505, Japan
  • Toru Miyama
    Japan Agency for Marine-Earth Science and Technology, Yokohama, Kanagawa 236-0001, Japan
  • Sergey Varlamov
    Japan Agency for Marine-Earth Science and Technology, Yokohama, Kanagawa 236-0001, Japan
  • Xinyu Guo
    Japan Agency for Marine-Earth Science and Technology, Yokohama, Kanagawa 236-0001, Japan
  • Takuji Waseda
    Japan Agency for Marine-Earth Science and Technology, Yokohama, Kanagawa 236-0001, Japan
  • Sourav Sil
    Japan Agency for Marine-Earth Science and Technology, Yokohama, Kanagawa 236-0001, Japan

説明

<jats:p>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.</jats:p>

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