The integration of large AI models and traditional NWP models can be further improved by focusing on several key strategies:
Hybrid Model Development: Developing hybrid models that combine the strengths of AI models in capturing complex patterns and the physical principles of traditional NWP models can lead to more accurate and reliable forecasts. By integrating physical constraints and equations into AI models, it is possible to enhance the predictive capabilities while ensuring that the forecasts align with known atmospheric dynamics.
Data Assimilation Techniques: Enhancing data assimilation techniques within AI models can help improve the accuracy of initial conditions. By incorporating real-time observational data and improving quality control processes, AI models can generate more reliable forecasts. This integration can help reduce the dependency on dynamical model analysis data and prioritize real-time data input for practical scenarios.
Ensemble Forecasting: Implementing ensemble forecasting approaches that account for uncertainties in initial conditions is crucial. By generating multiple possible initial fields and producing probabilistic forecasts, AI models can provide additional guidance for decision-making. Designing perturbation schemes aligned with the characteristics of AI models and physical laws can help mitigate sampling uncertainty and improve the reliability of ensemble forecasts.
Physical Information-Guided Design: Ensuring that AI models correctly express physical principles, such as continuity equations, is essential. By focusing on building AI and physics-fused hybrid models, it is possible to address the challenges of accurately simulating atmospheric phenomena while leveraging the strengths of AI in capturing complex patterns and relationships in the data.
By implementing these strategies, the integration of large AI models and traditional NWP models can be optimized to leverage the strengths of both approaches and address their respective limitations effectively.