Meteorologists

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 9 task statements that define Meteorologists (ISCO-08 2112) score an average of 0.54 on a 0–1 exposure scale — more exposed than about 91% of the 427 placed occupations. Roughly 100% of its tasks fall somewhere on the exposed part of the gradient, and the typical task lands in the Gradient 3 band.

Exposure is task overlap, not a verdict. A high score means a generative-AI model can do part of the content of these tasks — it says nothing about whether the work is automated, whether anyone uses AI for it today, or whether jobs are lost. The gradient is scored on the international ISCO-08 system; the rest of Singulariki is U.S. O*NET/SOC, bridged below by an approximate, many-to-many crosswalk.

How its tasks split across the gradient

Each of the 9 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Studying data collected from meteorological stations, radar and satellite imagery and computer model output to plot and forecast weather conditions;”

Scores 0.65 on the 2025 scale. The task "Studying data collected from meteorological stations, radar and satellite imagery and computer model output to plot and forecast weather conditions" involves significant data analysis and interpretation, similar to the related task of "Performing measurements, analyzing and forecasting wind speed and direction" which received a score of 0.65. Generative AI can contribute significantly to data processing, pattern recognition, and initial forecasting by handling large datasets and applying predictive models. However, the need for human expertise to interpret complex weather data, contextually adjust forecasts, and respond to unexpected weather patterns remains crucial. The statistical and technical nature of this meteorological task, combined with the structured datasets used, align the task closely with those experiencing moderate to high automation potential in data-focused domains, yet still require professional oversight for accurate application. Given that Poland is a high-income country with access to modern technological tools, the potential for partial automation is significant, but it acknowledges the indispensable role of human expertise in refining forecasts and ensuring their accuracy. The slightly reduced score from the similar task’s 0.65 reflects the nuances requiring expert judgment beyond automated processing capabilities.

Moving fastest, 2023 → 2025

“Engaging in the design and development of new equipment and procedures for meteorological data collection, remote sensing, or for related applications;”

Model capability on this task changed by +0.40 in two years — the gradient is not static, it is filling in.

U.S. occupations this maps to

The American O*NET/SOC roles that crosswalk to ISCO-08 2112, biggest by employment first, via the published (approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 correspondence. These are the closest U.S. matches — not an asserted one-to-one identity.

• Physical Scientists, All Other
• Remote Sensing Scientists and Technologists
• Atmospheric and Space Scientists

In context

Part of the 2 - Professionals major group. Return to the full gradient to see how the whole group sits.

Meteorologists sit at the 91st percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Meteorologists rank in the 91st percentile for GenAI task exposure — overlap with what generative AI can attempt, not a projection of displacement. (ILO / Gmyrek et al. (2025) GenAI exposure gradient)
• About 100% of this occupation's tasks fall into an exposed gradient band. (ILO / Gmyrek et al. (2025))
• Mean task exposure rose by 0.09 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Studying data collected from meteorological stations, radar and satellite imagery and computer model output to plot and forecast weather conditions;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Meteorologists". https://singulariki.com/gradient/2112-meteorologists.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.