Assessing the Viability of GraphCast Tangent Linear and Adjoint Models for Application in a 4DVar Data Assimilation System

The application of machine learning (ML) models in meteorology has attracted significant attention due to their potential to enhance weather forecasting efficiency and accuracy. GraphCast, an ML-based weather model, presents a promising example of such innovation. However, its suitability for data assimilation (DA) systems, particularly the four-dimensional variational (4DVar) DA system, requires thorough evaluation. This study investigates the tangent linear (TL) and adjoint (AD) models of GraphCast to determine their viability for integration into a 4DVar DA framework.

We compare the results of GraphCast TL/AD models with those of the Model for Prediction Across Scales - Atmosphere (MPAS-A), a well-established numerical weather prediction (NWP) model. The comparison focuses on the physical consistency and reliability of the TL/AD responses to perturbations. Initial findings suggest that the GraphCast TL/AD models exhibit several unphysical properties that could undermine the effectiveness of a 4DVar DA system.

The implications of this study extend beyond 4DVar applications. Unphysical behavior reflected in ML-derived TL/AD models also leads to inaccurate error covariances and unreliable ensemble forecasts, potentially degrading the overall performance of ensemble-based DA systems. Addressing these challenges is crucial for ensuring that ML models can be reliably integrated into operational DA systems, paving the way for more accurate and efficient weather predictions.