第49回統計地震学セミナー / The 49th Statistical Seismology Seminar

The accuracy of weather forecasts has increased substantially over the past decades. This is due to at least three factors: The increase in computing power which allows a higher accuracy in solving the equations of the underlying physical model, a denser set of observations of the state of the atmosphere and better methods for data assimilation, that is the use of these observations to adjust the initial conditions of the model sequentially. In order to represent the uncertainty in assimilation and forecasting, ensembles are used whose members represent different states of the atmosphere that are compatible with the observations and the physical dynamics. Such ensemble methods should be viewed as Monte Carlo methods which provide the link to statistics.

Recently there has been interest in using these data assimilation tools also in seismology in order to improve forecasts and quantify their uncertainty. In this talk I will discuss some of the attempts in this direction. Time permitting, I will also present some new ideas for ensemble data assimilation.