Weather forecast is an infrastructure of national welfare and people's livelihood. Rapidly and accurately predicting and tracking extreme weather and climate events are of great significance to national security, agricultural production, etc. Wind and solar resource predictions from weather forecasts play an essential role in the site selection and power prediction for wind and solar power plants. It is the cornerstone for new energy to increase power generation and consumption rate, ensuring high-quality operations. Weather forecast capability also improves the ability to monitor and evaluate greenhouse effects from the climate change perspective, providing more scientific support for achieving the carbon peak and neutrality goals. As a research topic, it promotes the progress of fundamental sciences such as supercomputer architecture and scientific computing, with high research values and community impacts. Moreover, weather prediction also benefits many other industries, including aviation, transportation, agriculture, logistics, finance, etc.
Weather forecasting has multiple physical modes (each corresponds to a set of differential equations), usually solved on supercomputers. Chinese institutions and universities mostly use the Weather Research and Forecasting (WRF) model system. This model, on the other hand, is rather outdated and has a restricted performance. Therefore, data from other institutions, such as European Center for Medium-Range Weather Forecasts (ECMWF) and Global Forecast System (GFS), are often purchased and deployed to improve the forecasts. Unfortunately, the most accurate data or state-of-the-art models are often under export control and are publicly inaccessible. The rise of AI has provided scientific computing with new thinking perspectives and mathematical tools to help us solve previously unattainable problems. There is a great opportunity for us to overtake in the computational speed and accuracy for numerical weather predictions, with our strengths in AI.
The project aims to provide more efficient weather and climate policy with the integration of many single-physics and multi-physis models, which enables rapid inference, data assimilation, model correction, and accurate model selection based on temporal and geographic characteristics. The research focuses will include but are not limited to the following topics:
- Specific optimization on the numerical methods and low-level numerical linear algebra routines;
- Parallelization and performance optimization on High-Performance Computing (HPC) systems, especially on the emerging heterogeneous architectures;
- AI corrections on the numerical model and its predictions, including physical parameterizations;
- AI accelerations or simplifications for data assimilation techniques, such as 4D-Var and Kalman filters;
- AI accelerations for numerical simulations, including physical parameterizations and directly solving the underlying PDEs;
Related Research Topics
High-resolution numerical weather prediction often relies on massive computational grids, which poses an extremely high computational burden on the simulation. As a result, a variety of critical factors have to be considered in order to achieve better efficiency, such as how to optimize the low-level linear algebra routines, code parallelization, resources optimization, and how to speed up the solution procedure for differential equations. Specific directions in demand are:
- Numerical methods for meteorological PDEs, CPU/GPU code parallelization;
- Use AI to correct the physical models and to accelerate the numerical simulations at the algorithm level.