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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

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.