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Improving the operational forecasting system of the stratified flow in Osaka Bay using an ensemble Kalman filter–based steady state Kalman filter

机译：使用基于集合卡尔曼滤波器的稳态卡尔曼滤波器改进大阪湾分层流运行预测系统

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Numerical models of a water system are always based on assumptions and simplifications that may result in errors in the model's predictions. Such errors can be reduced through the use of data assimilation and thus can significantly improve the success rate of the predictions and operational forecasts. The ensemble Kalman filter (EnKF) is a generic data assimilation method which is suited for highly nonlinear models. However, for three-dimensional operational systems such as in the case of Osaka Bay, Japan, a full EnKF would be computationally too demanding. In the present paper, a steady state Kalman filter (SSKF) simplification based on the correlation scales derived from the EnKF is proposed. This EnKF-based SSKF (EnSSKF) as presented in this paper is applied in combination with the three-dimensional Delft3D-FLOW system, modeling the stratified circulation system of Osaka Bay in Japan. The aim of the application of the EnSSKF is to improve the daily operational forecasts of salinity and current profiles for engineering activities within the basin. Salinity and velocity components were assimilated on an hourly basis for the period 13–28 February 2002. The results of the filter performance and its forecasting ability are presented. The performance of the EnSSKF for improving the profiles of salinity and velocity components forecast during the first 24 h forecast is illustrated.
机译：水系统的数值模型始终基于假设和简化，这些假设和简化可能会导致模型预测中的错误。可以通过使用数据同化来减少此类错误，从而可以显着提高预测和操作预测的成功率。集合卡尔曼滤波器（EnKF）是一种通用的数据同化方法，适用于高度非线性的模型。但是，对于三维操作系统（例如日本大阪湾），完整的EnKF在计算上会要求很高。在本文中，提出了一种基于从EnKF得出的相关标度的稳态卡尔曼滤波器（SSKF）的简化方法。本文提出的基于EnKF的SSKF（EnSSKF）与三维Delft3D-FLOW系统结合使用，对日本大阪湾的分层环流系统进行建模。 EnSSKF的应用目的是改善流域内工程活动的盐度和当前剖面的日常运行预测。在2002年2月13日至28日期间，每小时对盐度和速度分量进行同化。给出了过滤器性能及其预测能力的结果。说明了EnSSKF在前24小时预报过程中改善盐度和速度分量预报曲线的性能。

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