An Ensemble Kalman Filter Data Assimilation Method for the Sea Surface Temperature in the China Seas: Implementation and Simulation Experiments

Abstract

Abstract Data assimilation refers to a method of integrating observation data in the dynamic operation of numerical models on the basis of considering the temporal and spatial distribution of data and the error of observation field and background field. The Ensemble Kalman filter (EnKF) as a technology that has been widely used in the field of atmosphere and ocean has been applied to the ROMS (the Regional Ocean Modeling System) for predicting Sea surface temperature in Yellow, and East China Seas. In order to explore the applicability and effectiveness of the EnKF method for improving the accuracy of marine numerical model, the Sea surface temperature (SST) gained from buoy were used to conduct data assimilation process with EnKF method. Twin experiments have been performed to analysis the sensitivity of this system to the ensemble size and errors in model simulation and observations and a real data assimilation scheme has been conducted to hindcast the SST at the Yellow, and East China Seas during the July of year 2014. The updated results after data assimilation indicate that the model simulation fits observation better when the forecast was updated by observations. The result show that EnKF can effectively reduce the simulation error of complex numerical marine models.