Abstract A high-resolution ocean circulation model for the Indian Ocean (IO) using Regional Ocean Modeling System (ROMS) is operational at Indian National Centre for Ocean Information Services (INCOIS) which provides ocean state forecasts for the Bay of Bengal (BoB) and the Arabian Sea (AS) to the Indian Ocean rim countries. To provide an improved estimate of ocean state, a variant of Ensemble Kalman Filter (EnKF), viz., the Local Ensemble Transform Kalman Filter (LETKF) has been developed and interfaced with the present basin-wide operational ROMS. This system assimilates in-situ temperature and salinity profiles and satellite track data of sea-surface temperature (SST). The ensemble members of the assimilation system are initialized with different parameters like diffusion and viscosity coefficients and are subjected to an ensemble of atmospheric fluxes. In addition, one half of the ensemble members respond to K profile parameterization mixing scheme while the other half is subjected to Mellor–Yamada mixing scheme. This strategy aids in arresting the filter divergence which has always been a challenging task. The assimilated system simulates the ocean state better than the present operational ROMS. Improvements permeate to deeper ocean depths with better correlation and reduced root-mean-squared deviation (RMSD) with respect to observations particularly in the northern Indian Ocean which is data rich in density. Analysis shows domain averaged RMSD reduction of about 0.2–0.4 °C in sea surface temperature and 2–4 cm in sea level anomaly. The assimilated system also manages to significantly improve the thickness of the temperature inversion layers and the duration of its occurrence in northern Bay of Bengal. The most profound improvements are seen in currents, with an error reduction of 15 cm/s in zonal currents of central Bay of Bengal.

中文翻译：

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基于集合的印度洋区域海洋数据同化系统：实施和评估

摘要 使用区域海洋建模系统 (ROMS) 的印度洋 (IO) 高分辨率海洋环流模型在印度国家海洋信息服务中心 (INCOIS) 运行，该中心提供孟加拉湾 (BoB) 和阿拉伯海 (AS) 到印度洋沿岸国家。为了提供对海洋状态的改进估计，集成卡尔曼滤波器 (EnKF) 的变体，即局部集成变换卡尔曼滤波器 (LETKF) 已开发并与当前的全盆地操作 ROMS 接口。该系统同化了原位温度和盐度剖面以及海面温度 (SST) 的卫星轨迹数据。同化系统的集合成员被初始化为不同的参数，如扩散和粘度系数，并受到大气通量集合的影响。此外，合奏成员的一半响应 K 剖面参数化混合方案，而另一半则服从 Mellor-Yamada 混合方案。该策略有助于阻止过滤器发散，这一直是一项具有挑战性的任务。同化系统比目前可操作的 ROMS 更好地模拟海洋状态。改进渗透到更深的海洋深度，具有更好的相关性和降低的均方根偏差 (RMSD)，尤其是在密度数据丰富的印度洋北部。分析显示域平均 RMSD 减少约 0.2-0。海面温度升高 4 °C，海平面异常升高 2–4 cm。同化系统还设法显着提高了逆温层的厚度及其在孟加拉湾北部的持续时间。最显着的改进出现在洋流中，孟加拉湾中部纬向洋流的误差减少了 15 厘米/秒。