Methods for improving weather and climate prediction capabilities
Numerical weather and climate modeling have long been key applications for supercomputers. Although increasing computing power over the years has helped to improve the simulation resolution as well as the size of ensembles for performing weather and climate modeling, the accuracy of weather and climate predictions is still quite limited. Deep learning techniques, as well as other big data methods, have demonstrated their potential in various application domains.
In this project, we explore the potential benefits of combining high-resolution simulation with deep learning-based data analysis. The major goal is to improve either the prediction accuracy or the validity period of the prediction. The tasks for graduate students involved in this project include: (1) performing high-precision weather or climate simulations using the existing software on Sunway TaihuLight, and resolving the performance bottlenecks where possible; (2) utilizing the deep learning framework on Sunway TaihuLight to perform data analysis tasks of observation and reanalysis of data; (3) exploring potential methods of combining the deep learning data analysis parts into the simulation workflow, so as to improve the prediction capabilities in either weather or climate scenarios.
Students participating in this project should have a strong understanding of computer architecture and parallel computing. Applied domain knowledge and MPI experience is also acceptable experience.
Keywords: Climate/weather; high-precision simulations; big data analytics
Mentors: Wei Xue (Tsinghua University) and Haohuan Fu (Tsinghua University)