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Wenfeng Lai, Jianping Gan, Ye Liu, Zhiqiang Liu, Jiping Xie, and Jiang Zhu

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.

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Shiqiu Peng, Yineng Li, Xiangqian Gu, Shumin Chen, Dongxiao Wang, Hui Wang, Shuwen Zhang, Weihua Lv, Chunzai Wang, Bei Liu, Duanling Liu, Zhijuan Lai, Wenfeng Lai, Shengan Wang, Yerong Feng, and Junfeng Zhang

Abstract

A real-time regional forecasting system for the South China Sea (SCS), called the Experimental Platform of Marine Environment Forecasting (EPMEF), is introduced in this paper. EPMEF consists of a regional atmosphere model, a regional ocean model, and a wave model, and performs a real-time run four times a day. Output from the Global Forecast System (GFS) from the National Centers for Environmental Prediction (NCEP) is used as the initial and boundary conditions of two nested domains of the atmosphere model, which can exert a constraint on the development of small- and mesoscale atmospheric perturbations through dynamical downscaling. The forecasted winds at 10-m height from the atmosphere model are used to drive the ocean and wave models. As an initial evaluation, a census on the track predictions of 44 tropical cyclones (TCs) during 2011–13 indicates that the performance of EPMEF is very encouraging and comparable to those of other official agencies worldwide. In particular, EPMEF successfully predicted several abnormal typhoon tracks including the sharp recurving of Megi (2010) and the looping of Roke (2011). Further analysis reveals that the dynamically downscaled GFS forecasts from the most updated forecast cycle and the optimal combination of different microphysics and PBL schemes primarily contribute to the good performance of EPMEF in TC track forecasting. EPMEF, established primarily for research purposes with the potential to be implemented into operations, provides valuable information not only to the operational forecasters of local marine/meteorological agencies or international TC forecast centers, but also to other stakeholders such as the fishing industry and insurance companies.

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