Author(s): Ghada El Serafy; Herman Gerritsen; Arthur Mynett; Masahiro Tanaka
Keywords: Data assimilation; Operational forecasting; Water systems; Stratified flows
Abstract: 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 by calibration of the model to insitu and/or satellite measurements of the system’s state and by integration of models and data. Use of the Ensemble Kalman Filter (EnKF) Evensen, (1994) with recent measurement data in operational forecast situations will significantly improve the success rate of the predictions. The EnKF is a generic data assimilation method which is also suited for highly non-linear models. The EnKF was applied for a one dimensional hydrodynamic model and proven its potential in improving the forecast, El Serafy and Mynett, (2004). However, for three dimensional operational systems as in the case of Osaka Bay, Japan, a full EnKF was computationally too demanding, thus a simplification based on the correlation scales derived from the EnKF was chosen. In the present paper, a Steady State Kalman filter (SSKF) was implemented and applied in combination with a three dimensional Delft3D-Flow system, Lesser et al. (2004), modelling the Osaka Bay. The bay is a stratified three-dimensional circulation system. The aim of the application of SSKF is to improve the daily operational forecasts of salinity and current profiles for engineering activities in this stratified basin. Salinity and velocity components were assimilated for the period of 13-28 of Feb. 2002 on an 796 September 11~16, 2005, Seoul, Korea hourly basis. The results of the filter and its forecasting ability are presented. The performance of the SSKF for improving the profiles of salinity and velocity components forecast during the first 24 hours forecast is illustrated.