Ensemble forecasting is a key part of weather forecasting today. Computers typically run multiple simulations, called ensembles, using slightly different initial conditions or assumptions, and then analyze them together to try to improve forecasts. Now, in research published in Geophysical Research Letters, using Japan’s flagship 10-petaFLOPS K computer, researchers from the RIKEN Advanced Institute for Computational Science (AICS) have succeeded in running 10,240 parallel simulations of global weather, the largest number ever performed, using data assimilation to reduce the range of uncertainties.

The assimilation of the 10,240 ensemble data sets was made possible by a cross-disciplinary collaboration of data assimilation experts and eigenvalue solver scientists at RIKEN AICS.

The “Local Ensemble Transform Kalman Filter” (LETKF), an already efficient system, was further improved by a factor of eight using the “EigenExa” high-performance eigenvalue solver software, making possible a three-week computation of data from the 10,240 ensembles for simulated global weather. By analyzing the 10,240 equally probable estimates of atmospheric states, the team discovered that faraway observations, even going beyond 10,000 kilometers in distance, may have an immediate impact on eventual state of the estimation. This finding suggests the need for further research on advanced methods that can make better use of faraway observations, as this could potentially lead to an improvement of weather forecasts.

The following three research projects funded by the Japan Science and Technology Agency (JST) CREST programs contributed to this achievement:

• “Innovating ‘Big Data Assimilation’ technology for revolutionizing very-short-range severe weather prediction” (led by Dr. Takemasa Miyoshi of RIKEN), a project in the research area of Advanced Application Technologies to Boost Big Data Utilization for Multiple-Field Scientific Discovery and Social Problem Solving (Research Supervisor: Prof. Yuzuru Tanaka of Hokkaido University)
• “EBD: Extreme Big Data: Convergence of Big Data and HPC for Yottabyte Processing” (led by Prof. Satoshi Matsuoka of the Tokyo Institute of Technology with Dr. Takemasa Miyoshi of RIKEN acting as co-PI), which is a project in the Advanced Core Technologies for Big Data Integration area (Research Supervisor: Prof. Masaru Kitsuregawa of the National Institute of Informatics)
• “Development of an Eigen-Supercomputing Engine using a Post-Petascale Hierarchical Model” (led by Prof. Tetsuya Sakurai of the University of Tsukuba with Dr. Toshiyuki Imamura of RIKEN acting as co-PI), a project in the Development of System Software Technologies for post-Peta Scale High Performance Computing (Research Supervisor: Dr. Akinori Yonezawa of RIKEN)

Reference: T. Miyoshi, K. Kondo, and T. Imamura “The 10240-member ensemble Kalman filtering with an intermediate AGCM”. Geophysical Research Letters, 2014, doi:10.1002/2014GL060863