Researchers Integrate Physics, AI to Enhance Precipitation Forecast
The study, published in the journal Geophysical Research Letters, was conducted by a research team led by the Institute of Atmospheric Physics (IAP) of the Chinese Academy of Sciences.
According to the study, in the AI era, pure data-driven meteorological and climate models are gradually catching up with, and even surpassing, traditional numerical models. However, significant challenges persist in current deep learning models, which hinder the predictive capabilities for complex weather and climate phenomena, including precipitation.
The researchers proposed a new approach to address these challenges that involves combining physics, atmospheric dynamics, and deep learning models.
Leveraging EarthLab, a new Earth System Science Numerical Simulator Facility developed by the IAP, the team employed data and computational power to enhance numerical models' precipitation forecasting skills.
They focused on coupling physical variables through graph neural networks to introduce physical constraints and improve the accuracy of precipitation forecasts.
In the AI era, the integration of physics is a major challenge with various approaches and perspectives, said Huang Gang, the corresponding author of the paper.
"Our team, drawing on atmospheric and climate dynamics considerations, has experimented with applying soft constraints to models from a physical coupling perspective," Huang added.
4155/v
