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Physics of estuaries and coastal seas
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Assimilating remote sensing and in situ observations into a coastal ocean model using ensemble optimal interpolation
P-P1-06
Wenfeng Lai* , Hong Kong University of Science and Technology
Ye Liu, Swedish Meteorological and Hydrological Institute
Jianping Gan, Hong Kong University of Science and Technology
Zhiqiang Liu, Hong Kong University of Science and Technology
Jiang Zhu, The Institute of Atmospheric Physics,Chinese Academy of Sciences
Presenter Email: laiwf@ust.hk |
| To improve the forecasting performance of the water around Hong Kong, a multivariable data assimilation (DA) system using the ensemble optimal interpolation (EnOI) method has been developed and adopted for a high-resolution estuary-shelf ocean model around Hong Kong. A data assimilation experiment was conducted by using the ROMS model during the cruise in July, 2015. The assimilated data include high-resolution sea surface temperature from the Operational SST and Sea Ice Analysis (OSTIA) and in-situ conductive-temperature-depth (CTD) observations. Based on analysis of spatiotemporal correlation among ocean parameters, optimal assimilation scheme was identified. By assimilating SST and in situ CTD hydrographic profiles, the root mean square errors (RMSEs) between the DA forecasts and observations for temperature and salinity have been reduced by 23.5% and 14.0% in the experiment period, respectively. We found that by adjusting localization radius according to a weight function of observations, by considering the intra-tidal variation of the observed data and by increasing observation samples and model states for error covariance, it improved significantly the DA skill and reduced the model error. |
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