Regimes of Scale in AI Meteorology
TL;DR
It is argued 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.
Abstract
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 18 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 systems 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.
