Applying physically coupled graph neural networks to improve precipitation forecasting skills at the Institute of Atmospheric Physics, Chinese Academy of Sciences

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In the era of large models, the effects of purely data-driven meteorological and climate models have gradually caught up with or even surpassed numerical models.

However, existing large-scale meteorological and climate models still have some problems. For example, the physical consistency in the model is not high enough, which limits the ability to predict complex weather and climate phenomena such as precipitation. In addition, the forecast effect of divergent wind is not satisfactory. These issues require further research and improvement to improve the prediction accuracy and reliability of the model.

At present, combining physics, atmospheric dynamics and deep learning models is an important way to solve the bottleneck problem.

Recently, the team of researcher Huang Gang from the Institute of Atmospheric Physics, Chinese Academy of Sciences, based on the data and computing power support of the Earth System Numerical Simulation Device (Huan), starting from the perspective of the coupling relationship of physical variables, combined with the figure The neural network imposes physical soft constraints on multiple variables, improves the precipitation forecasting skills of numerical models, and makes some attempts and explorations in the integration of physics and AI.

The research was titled "Coupling Physical Factors for Precipitation Forecast in China With Graph Neural Network" and was published in "Geophysical Research Letters" on January 18.

Paper link: https://doi.org/10.1029/2023GL106676

In response to the difficult issues of precipitation forecasting, especially the forecasting of heavy precipitation, Our team solved the problem by starting from the influencing factors and occurrence mechanism of precipitation, combining the omega equation and the water vapor equation, etc., to screen variables and build a variable coupling graph network.

The Omega equation and the water vapor equation describe vertical movement and water vapor changes respectively, both of which are important factors affecting precipitation. From the perspective of graph network, the aforementioned equation reflects the relationship between the nonlinear combination of basic physical quantities (temperature, wind, humidity, etc.) and the key elements of precipitation. Therefore, it can be abstracted into a graph network, and through the variables between the graph network (nodes) and the relationships between variables to represent the combination and coupling between different physical variables.

At the same time, taking into account the impact of climate factors on weather scales, especially the systematic differences in model errors under different climate backgrounds, this study combined sparse data such as season, ENSO and other climate factors and reporting time Use entity embedding technology to embed the correction model to distinguish errors in different backgrounds.

In addition, for the precipitation process, this study made local improvements to the graph neural network ChebNet, so that it can basically maintain the effect while avoiding global operations and greatly reducing the computational complexity.

Figure 1: Schematic of the omega-GNN model. (Source: paper)

Model comparison results show that the two physically constrained models omega-GNN and omega-EGNN proposed in this study significantly improve the precipitation forecasting skills of each category compared with the numerical model, and at the same time their performance Better than the current mainstream deep learning models without physical constraints (such as U-NET, 3D-CNN, etc.).

In addition, this study performed ten sets of perturbations on all deep learning models, allowing them to perform ensemble forecasts. Combining diagnosis and case analysis, it is found that the model with physical constraints is significantly better than the model without physical constraints. For the prediction of heavy precipitation, the consistency between the omega-GNN model and the omega-EGNN model ensemble is higher, and the forecasting skills are better.

Figure 2: Each model’s (a) TS score, (b-g) spatial distribution of TS differences relative to the numerical model (precipitation above the 20mm/6h threshold). (Source: paper)

Researcher Huang Gang, the corresponding author of the paper, said: "Our team has accumulated a lot in the direction of climate dynamics. In recent years, we have made some attempts to use AI to improve weather and climate predictions, and have achieved many related results. Won relevant competition awards for the first time. In the era of large AI models, how to integrate physics with AI is a big issue, and there are many ways and ideas for integration. We combined some thoughts on atmosphere and climate dynamics to soft-soften the model from the perspective of physical coupling. Constraints, some attempts have been made in this direction, hoping to provide some incremental information for related fields."

The research was conducted by master students Chen Yutong, Dr. Wang Ya, Researcher Huang Gang and the Institute of Atmospheric Physics, Chinese Academy of Sciences Completed in collaboration with Dr. Tian Qun from the Guangzhou Institute of Tropical Marine Meteorology, China Meteorological Administration.

Reference content: https://iap.cas.cn/gb/xwdt/kyjz/202401/t20240119_6959543.html

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