## Introducing DA

### 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 supercomputers, based on our previous achievements in meteorological applications. Our research is contributing to improvement of large-scale meteorological simulations, as well as facilitating broader applications of data assimilation.

### Applications

#### Prediction of Sudden Rainfall

Simulating sudden torrential rains with the K computer and the supercomputer Fugaku

#### Other Examples

• Hydroelectric Dam Operation
• Sustainability
• Vegetation
• Manufacturing
• Cell fate
• Health Prediction
• Red-tide

### 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.

Note: the figure on the right is based on simulation results from the global cloud-resolving model NICAM and NASA satellite imagery. Courtesy of Ryuji Yoshida.

#### 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 Kalman Filter), and advanced data assimilation method, and have been using it to improve simulation accuracy.

LETKF:
The results of multiple simulations from slightly different initial conditions (orange circle) tend to diverge and become more uncertain (blue circle) over time. LETKF takes into account current observations (red circle) and so reduces the uncertainty (green circle). These more accurate results are then used to initialize new simulations.

#### 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.

A data assimilation method is used to estimate the impact of meteorological observation data such as temperature, humidity, winds and pressure near a typhoon core region taken during a reconnaissance flight by the US Navy. Among the 20 observation locations on the flight back (blue line in the left image, green dashed line in the right figure), most observations improved the forecasts, while a few slightly degraded it.

#### Towards Future Weather Forecasting

Our group is working to advance our data assimilation system and optimize it for the supercomputer "Fugaku". High performance computers like "Fugaku" can be regarded as conventional supercomputers that will be used for weather forecasting 10 years from now. In utilizing "Fugaku" 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.

Precipitation patterns over the ocean southeast of Japan, analyzed by our data assimilation system based on the Weather Research and Forecasting (WRF) model. Using the K computer to perform data assimilation at a higher resolution (finger grid cells) results in a more precise precipitation pattern.