Stability of large-scale neural autoregressive models of geophysical turbulence (Online)

Abstract

Recent efforts in building data-driven surrogates for weather forecasting applications have received a lot of attention and garnered noticeable success. These autoregressive data-driven models yield significantly competitive short-term forecasting performance (often outperforming traditional numerical weather models) at a fraction of the computational cost of numerical models. However, these data-driven models do not remain stable when time-integrated for a long time. Such a long time-integration would provide (1) a method to seamlessly scale a weather model to a climate model and (2) gathering insights into the statistics of that climate system, e.g., the extreme events, owing to the cheap cost of generating multiple ensembles. While many studies have reported this instability, especially for data-driven models of turbulent flow, a causal mechanism for this instability is not clear. Most efforts to obtain stability are ad-hoc and empirical. In this work, we use a canonical quasi-geost...