The weather system is chaotic and known to be sensitive to the initial conditions. Therefore, it is important to estimate accurately the current state of the atmosphere for more accurate initial conditions. Data assimilation is a statistical approach to estimate the best possible atmospheric state using both simulation and observation data. The local ensemble transform Kalman filter (LETKF) is an advanced data assimilation method that is particularly efficient with parallel architecture computers.
Non-hydrostatic icosahedral atmospheric model (NICAM) is a weather forecasting model and has been developed by Computational Climate Science Research Team in AICS. Data Assimilation Research Team works collaboratively and applied the LETKF to the NICAM for assimilating various kinds of observations. We are developing a new system for assimilating satellite data. In this talk I will give a brief and clear introduction to data assimilation and present the most recent research on the newly-developed NICAM-LETKF system．