Regimes of Scale in AI Meteorology

TL;DR

This paper analyzes how AI/ML tools are integrated into meteorology, highlighting the challenges posed by differing data and modeling scales rooted in distinct infrastructural regimes.

Contribution

It introduces the concept of 'regimes of scale' to explain tensions in AI/ML adoption within meteorology, emphasizing infrastructural and organizational differences.

Findings

AI/ML methods face integration challenges due to infrastructural differences.

Meteorology's data and models are organized differently from AI/ML platforms.

AI/ML tools struggle to adapt to meteorological regimes of scale.

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 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.