In January 2024, a Japanese lunar lander successfully touched down on the moon as part of the Smart Lander for Investigating Moon (SLIM) mission. Accompanying the SLIM on its journey were two small rovers named lunar excursion vehicles (LEV-1 and 2). LEV-2, a transformable nano rover, was particularly designed to capture images of the SLIM on the lunar surface. During the mission, LEV-2 successfully maneuvered and captured images of the lander. Understanding the mobility characteristics of LEV-2 was crucial for the success of this task. This study analyzed the mobility characteristics of LEV-2 through numerical simulation and traveling tests. In the numerical simulation, a dynamics model of LEV-2 was first developed. Subsequently, the motion behaviors of LEV2 were analyzed by utilizing the discrete element method. Traveling tests were then conducted using the LEV-2 engineering model on various terrains covered with a lunar regolith simulant. The results of both the numerical simulations and experiments revealed consistent trends across different moving modes of LEV-2. Furthermore, the simulation results indicated no significant deterioration in the mobility performance of LEV-2 on the moon when compared to that on the Earth. Based on the characteristics predicted from the simulation and the quantitative data obtained from the experiments, it was concluded that LEV-2 is supposed to travel at least 15◦slopes on the moon.

Numerical weather prediction (NWP) supports our daily lives. Weather models require higher spatiotemporal resolutions to prepare for extreme weather disasters and reduce the uncertainty of predictions. The accuracy of the initial state of the weather simulation is also critical; thus, we need more advanced data assimilation (DA) technology. By combining resolution and ensemble size, we have achieved the world’s largest weather DA experiment using a global cloud-resolving model and an ensemble Kalman filter method. The number of grid points was $\sim$4.4 trillion, and 1.3 PiB of data was passed from the model simulation part to the DA part. We adopted a data-centric application design and approximate computing to speed up the overall system of DA. Our DA system, named NICAM-LETKF, scales to 131,072 nodes (6,291,456 cores) of the supercomputer Fugaku with a sustained performance of 29 PFLOPS and 79 PFLOPS for the simulation and DA parts, respectively.