Track-Dependent Links between Tropical Cyclones and Extratropical Predictability in Physical and AI Models

TL;DR

This study compares physics-based and AI-hybrid models in predicting tropical cyclone impacts on extratropical weather forecasts, revealing track-dependent effects and demonstrating the AI model's utility in predictability research.

Contribution

It introduces an AI-hybrid model capable of capturing tropical convection effects and analyzes track-dependent forecast impacts, advancing understanding of tropical-extratropical teleconnections.

Findings

AI-hybrid model performs comparably to physics-based models.

Tropical cyclone impacts on forecasts are highly track-dependent.

Westward-moving TCs significantly degrade Week-2 forecasts.

Abstract

Global medium-range weather forecasts suffer occasional failures, often linked to tropical cyclones (TCs). We investigate TC influences on extratropical predictability by comparing forecasts from a physics-based model (ECMWF-IFS) and an AI-hybrid model (Google-NGCM) initialized near TC genesis. Analyzing 108 out-of-sample Northern Hemisphere cases reveals similar extratropical error growth patterns and comparable performance between the models. This suggests that the NGCM is capable of predicting the bulk upscale effects of tropical convection without directly representing convective processes. Leveraging the NGCM's computational efficiency, we compare forecasts initialized with and without TC genesis to isolate track-dependent forecast impacts. For Week-2 extratropical forecasts, TC impacts are highly time-, metric-, and track-dependent. The analysis confirms that some poleward-moving TCs degrade Week-2 US and European forecasts and suggests significant impacts from westward-moving TCs. The findings highlight the utility of the AI-hybrid model in predictability research and complex tropical-extratropical teleconnections that warrant future research.