Assimilating in situ and remote sensing observations in a highly variable estuary-shelf model

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  • 1 Department of Mathematics and Department of Ocean Science, Hong Kong University of Science and Technology, Hong Kong
  • 2 Swedish Meteorological and Hydrological Institute, Norrköping, Sweden
  • 3 Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, China
  • 4 Nansen Environmental and Remote Sensing Center, Bergen, Norway
  • 5 Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
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Abstract

To improve the forecasting performance in dynamically active coastal waters forced by winds, tides, and river discharges in a coupled estuary-shelf model off Hong Kong, a multivariable data assimilation (DA) system using the ensemble optimal interpolation (EnOI) method has been developed and implemented. The system assimilates the Conductivity-Temperature-Depth (CTD) profilers, time-series buoy measurement, and remote sensing sea surface temperature (SST) data into a high-resolution estuary-shelf ocean model around Hong Kong. We found that the time window selection associated with the local dynamics and the number of observation samples are two key factors in improving assimilation in the unique estuary-shelf system. DA with a varied assimilation time window based on the intra-tidal variation in the local dynamics can reduce the errors in the estimation of the innovation vector caused by the model-observation mismatch at the analysis time, and improve greatly simulation in both the estuary and coastal regions. Statistically, the overall root-mean-square error (RMSE) between the DA forecasts and not-yet-assimilated observations for temperature and salinity have been reduced by 33.0% and 31.9% in the experiment period, respectively. By assimilating higher resolution remote sensing SST data instead of lower resolution satellite SST, the RMSE of SST is improved by ~18%. Besides, by assimilating real-time buoy mooring data, the model bias can be continuously corrected both around the buoy location and beyond. The assimilation of the combined buoy, CTD, and SST data can provide an overall improvement of the simulated three-dimensional solution. A dynamics-oriented assimilation scheme is essential for the improvement of model forecasting in the estuary-shelf system under multiple forcings.

Corresponding author. E-mail address: magan@ust.hk.

Abstract

To improve the forecasting performance in dynamically active coastal waters forced by winds, tides, and river discharges in a coupled estuary-shelf model off Hong Kong, a multivariable data assimilation (DA) system using the ensemble optimal interpolation (EnOI) method has been developed and implemented. The system assimilates the Conductivity-Temperature-Depth (CTD) profilers, time-series buoy measurement, and remote sensing sea surface temperature (SST) data into a high-resolution estuary-shelf ocean model around Hong Kong. We found that the time window selection associated with the local dynamics and the number of observation samples are two key factors in improving assimilation in the unique estuary-shelf system. DA with a varied assimilation time window based on the intra-tidal variation in the local dynamics can reduce the errors in the estimation of the innovation vector caused by the model-observation mismatch at the analysis time, and improve greatly simulation in both the estuary and coastal regions. Statistically, the overall root-mean-square error (RMSE) between the DA forecasts and not-yet-assimilated observations for temperature and salinity have been reduced by 33.0% and 31.9% in the experiment period, respectively. By assimilating higher resolution remote sensing SST data instead of lower resolution satellite SST, the RMSE of SST is improved by ~18%. Besides, by assimilating real-time buoy mooring data, the model bias can be continuously corrected both around the buoy location and beyond. The assimilation of the combined buoy, CTD, and SST data can provide an overall improvement of the simulated three-dimensional solution. A dynamics-oriented assimilation scheme is essential for the improvement of model forecasting in the estuary-shelf system under multiple forcings.

Corresponding author. E-mail address: magan@ust.hk.
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