What is Data Assimilation?
Simulations are based on modeling the real world, therefore, gaps between simulations and the real world are inevitable. Data assimilation fills in the gaps by integrating actual observation data into simulations, which enhances a simulation's accuracy. We are developing advanced algorithms for data assimilation on the K computer, based on our previous achievements in meteorological applications. Our research is contributing to improvement of large-scale meteorological simulations, as well facilitating broader applications of data assimilation.
Forecasting Sudden Rainfall
Improving Weather Forecast
- Hydroelectric Dam Operation
- Brain waves
- Cell fate
Introduction to Data Assimilation
Function of Data Assimilation
Data assimilation is like a bridge between observation and simulations. In the case of weather forecasting, observations from radiosondes (weather balloons) and weather-observation satellites, for example, are integrated into numerical weather prediction simulations. As a result, we obtain more accurate weather forecasts.
Our Method of Data Assimilation
Data assimilation is based on applied mathematics and statistics, and it can be approached in a number of ways. Among others, we have been focusing on LETKF (Local Ensemble Transform Filter), and advanced data assimilation method, and have been using it to improve simulation accuracy.
Evaluating Impact of Observation Data
Data assimilation improves simulation accuracy by using observation data; it can also be employed to estimate quantitatively how much each datum contributes to the improvements. Such studies may lead to more efficient and effective observation strategies.
Towards Future Weather Forecasting
Our group is working to advance our data assimilation system and optimize it for the K computer. High performance computers like the K computer can be regarded as conventional supercomputers that will be used for weather forecasting 10 years from now. In utilizing the K computer today, we are preparing for the future by developing data assimilation techniques that can be implemented in the next 10 years for more accurate analysis and forecasts.