Regimes of Scale in AI Meteorology
This work addresses challenges in applying AI to meteorology, an incremental contribution to HCI literature on domain-specific AI integration.
The paper analyzed the integration of AI/ML tools in meteorology, revealing tensions due to differing 'regimes of scale' between AI and meteorological practices, which can hinder effective application.
HCI work has explored the effective integration of AI/ML tools across "application domains" from healthcare to finance to transportation. We add to this literature with an analysis of AI/ML tools in meteorology, a domain that already uses "big data" and massive physics-based models. Drawing from 12 interviews with forecasters and meteorologists with varied connections to AI/ML weather modeling, we trace tensions in AI/ML weather application arising from what we call "regimes of scale," different ways that AI/ML and meteorological regimes make observations, data, and models scale. Rather than seeing AI/ML as a domain-agnostic tool, we argue that AI/ML methods were born from specific platform and internet infrastructures, and so they can struggle to integrate with very different (in this case meteorological) ways of organizing data pipelines.