Occupation · SOC 19-2021.00
Investigate atmospheric phenomena and interpret meteorological data, gathered by surface and air stations, satellites, and radar to prepare reports and forecasts for public and other uses. Includes weather analysts and forecasters whose functions require the detailed knowledge of meteorology.
Also called: Forecaster · General Forecaster · Meteorologist · Research Meteorologist · Broadcast Meteorologist · Hydrometeorological Technician (Hydrometeorological Tech) · Ocean Monitoring and Data Assimilation Scientist · Service Hydrologist · Warning Coordination Meteorologist · Weather Forecaster · Aerologist · Air Analyst
Job family: Life, Physical, and Social Science Occupations
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-19-2021-00/context.md directly.
A fast read on where AI already shows up in this occupation, where it stays a copilot, where humans remain in the loop, and what the labor market is doing. Built from observed Claude.ai conversations mapped to O*NET tasks and from published research — measures of usage and exposure, not advice or predictions that the job is going away.
Task areas most often handled directively in observed AI conversations — candidates to delegate with light review.
Task areas where people work with AI — iterating, learning, or checking — staying in the loop rather than handing the task off.
Task areas where a human was still judged necessary in a large share of observed conversations — not a safety ruling, an observed-need signal.
The capabilities O*NET rates most important for this occupation — the human ground the work is built on.
See all skills →Independent published positions, read together — not a forecast.
96th-percentile task overlap — yet about 700 openings a year (+0.7% projected, BLS), and observed AI use leans 5097% copilot, not hand-off (AEI) . What exposure means →
What today's research says about this occupation's exposure to AI, how AI is actually being used in it, and where employment is headed. These are positions within published studies — measures of exposure and usage, not predictions that this job will disappear.
Each study uses its own scale, so the raw scores are not comparable across rows — the percentile (this job's rank among all U.S. occupations with data) is the comparable figure, and sizes the bars.
| Measure | Rank vs all occupations | Percentile | Score |
|---|---|---|---|
| Overall AI exposure (Felten et al.) High | 94th | 1.4 | |
| LLM task exposure, γ (OpenAI / Eloundou) High | 87th | 1.0 | |
| AI assistant applicability (Microsoft) High | 92nd | 0.3 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.2), with simple added tooling (β 0.6), and including AI-powered software (γ 1.0). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.
Most of this job's tasks can be done remotely (Dingel–Neiman), which tends to track with higher digital and AI exposure.
A pre-LLM (2013) estimate of how automatable this job is by computerization and robotics. Shown for historical context only — it is not part of any current AI ranking.
Frey–Osborne probability 0.7 · 56th percentile among occupations · Moderate
Among measured AI assistant conversations mapped to this occupation (Anthropic Economic Index, 2026-01-15), these task types came up most. These are shares of observed AI conversations — not shares of the job, of worker time, or of what could be automated.
| Interpret data, reports, maps, photographs, or charts to predict long- or short-range weather conditions, using computer models and knowledge of climate theory, physics, and mathematics. | 7.1% | |
| Prepare weather reports or maps for analysis, distribution, or use in weather broadcasts, using computer graphics. | 1.7% | |
| Gather data from sources such as surface or upper air stations, satellites, weather bureaus, or radar for use in meteorological reports or forecasts. | 0.8% | |
| Prepare scientific atmospheric or climate reports, articles, or texts. | 0.7% | |
| Conduct meteorological research into the processes or determinants of atmospheric phenomena, weather, or climate. | 0.7% | |
| Apply meteorological knowledge to issues such as global warming, pollution control, or ozone depletion. | 0.3% |
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | About average · +0.7% by 2034 |
| Projected annual openings | 700 |
| Employment 2024 → 2034 | 9,400 → 9,500 |
“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.
The ILO's 2025 global study scores generative-AI exposure on the international ISCO-08 occupation system, not US SOC. Bridged through the published (and approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 crosswalk, this US occupation corresponds to the international occupation below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| Meteorologists · 2112 | 54% | Gradient 3 |
Read the whole six-band gradient on the GenAI exposure gradient page. The crosswalk is approximate: a US occupation can map to several international ones, and the ILO scores describe the international occupation, not this exact US role.
How people actually apply AI to this occupation's tasks, from Claude.ai (Free and Pro) conversations in the Anthropic Economic Index, 2026-01-15. This is one AI assistant's consumer sample — not all AI, not the whole workforce. Autonomy and the collaboration mix are model-rated estimates; figures below the sample floor are hidden.
| Augmentation vs. automation | 51.0% working with AI · 24.5% handed to AI |
| Most common way people use AI here | Learning · you ask AI to explain or teach |
| Typical AI autonomy | 3.0 / 5 · higher = AI acts more independently |
| Used for work (vs. personal / coursework) | 23.4% |
The role's most common tasks in AI conversations, each tagged with how people work with the AI on it. “Usage” is the share of observed conversations, not of the job.
| Task | How | Usage |
|---|---|---|
| Interpret data, reports, maps, photographs, or charts to predict long- or short-range weather conditions, using computer models and knowledge of climate theory, physics, and mathematics. | Learning | 2.0% |
| Conduct meteorological research into the processes or determinants of atmospheric phenomena, weather, or climate. | Learning | 0.9% |
| Gather data from sources such as surface or upper air stations, satellites, weather bureaus, or radar for use in meteorological reports or forecasts. | Directive | 0.9% |
| Design or develop new equipment or methods for meteorological data collection, remote sensing, or related applications. | Directive | 0.5% |
| Prepare weather reports or maps for analysis, distribution, or use in weather broadcasts, using computer graphics. | Iteration | 0.4% |
Tasks where the model most often judged that a person remained necessary — a useful read on the current boundary, not a guarantee.
| Gather data from sources such as surface or upper air stations, satellites, weather bureaus, or radar for use in meteorological reports or forecasts. | 90.9% | |
| Interpret data, reports, maps, photographs, or charts to predict long- or short-range weather conditions, using computer models and knowledge of climate theory, physics, and mathematics. | 90.0% | |
| Conduct meteorological research into the processes or determinants of atmospheric phenomena, weather, or climate. | 84.3% | |
| Prepare weather reports or maps for analysis, distribution, or use in weather broadcasts, using computer graphics. | 76.3% | |
| Design or develop new equipment or methods for meteorological data collection, remote sensing, or related applications. | 71.1% |
Example prompts phrased from the tasks people most often delegate to AI in this occupation (Anthropic Economic Index). Each shows the underlying measured task and its share of observed AI use. They are suggested phrasings of real tasks — starting points, not endorsed instructions.
Help me interpret data, reports, maps, photographs, or charts to predict long- or short-range weather conditions, using computer models and knowledge of climate theory, physics, and mathematics. From: Interpret data, reports, maps, photographs, or charts to predict long- or short-range weather conditions, using computer models and knowledge of climate theory, physics, and mathematics. · 2.0% of measured AI use · learning
Help me conduct meteorological research into the processes or determinants of atmospheric phenomena, weather, or climate. From: Conduct meteorological research into the processes or determinants of atmospheric phenomena, weather, or climate. · 0.9% of measured AI use · learning
Help me gather data from sources such as surface or upper air stations, satellites, weather bureaus, or radar for use in meteorological reports or forecasts. From: Gather data from sources such as surface or upper air stations, satellites, weather bureaus, or radar for use in meteorological reports or forecasts. · 0.9% of measured AI use · directive
Help me design or develop new equipment or methods for meteorological data collection, remote sensing, or related applications. From: Design or develop new equipment or methods for meteorological data collection, remote sensing, or related applications. · 0.5% of measured AI use · directive
All 27 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
O*NET importance rating, from 1 (not important) to 5 (extremely important).
| Mathematics | 4.4 | |
| Physics | 4.4 | |
| Geography | 4.4 | |
| Computers and Electronics | 4.2 | |
| English Language | 3.8 | |
| Communications and Media | 3.2 | |
| Customer and Personal Service | 3.0 | |
| Chemistry | 3.0 |
| Oral Expression | 4.3 | |
| Oral Comprehension | 4.1 | |
| Written Comprehension | 4.1 | |
| Inductive Reasoning | 4.1 | |
| Deductive Reasoning | 4.0 | |
| Written Expression | 3.9 | |
| Problem Sensitivity | 3.9 | |
| Speech Clarity | 3.9 | |
| Information Ordering | 3.8 | |
| Near Vision | 3.8 | |
| Speech Recognition | 3.8 | |
| Mathematical Reasoning | 3.5 | |
| Category Flexibility | 3.4 | |
| Flexibility of Closure | 3.4 | |
| Fluency of Ideas | 3.3 |
| Reading Comprehension | 4.1 | |
| Active Listening | 4.0 | |
| Speaking | 4.0 | |
| Science | 4.0 | |
| Critical Thinking | 4.0 | |
| Active Learning | 4.0 | |
| Writing | 3.9 | |
| Mathematics | 3.4 | |
| Learning Strategies | 3.1 | |
| Monitoring | 3.1 |
| Complex Problem Solving | 3.9 | |
| Judgment and Decision Making | 3.8 | |
| Social Perceptiveness | 3.1 | |
| Systems Analysis | 3.1 | |
| Coordination | 3.0 | |
| Instructing | 3.0 | |
| Time Management | 3.0 |
Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.
Showing the top 40 of 45.
Showing the top 40 of 62.
How characteristic each condition is of the job, on O*NET's 1–5 context scale (higher = more present in day-to-day work). Each condition links to how it varies across all occupations.
What to study: Multi/Interdisciplinary Studies , Physical Sciences . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.
Share of people in this occupation at each level of education.
| Bachelor's Degree | 60.0% | |
| Master's Degree | 20.0% | |
| Doctoral Degree | 15.0% | |
| Post-Doctoral Training | 5.0% |
The interests and personal qualities O*NET associates with people who do this work.
| Physical Science | 6.8 | |
| Mathematics/Statistics | 5.9 | |
| Information Technology | 4.1 | |
| Engineering | 3.9 | |
| Public Speaking | 3.4 | |
| Media | 3.0 | |
| Teaching/Education | 2.4 |
| Investigative | 6.3 | |
| Realistic | 4.8 | |
| Conventional | 4.5 | |
| Artistic | 2.7 | |
| Enterprising | 2.7 | |
| Social | 2.5 |
| Dependability | 5.0 | |
| Attention to Detail | 4.0 | |
| Intellectual Curiosity | 3.0 |
U.S. · annual wages (BLS OEWS)
| 10th percentile | $49,990 |
| 25th percentile | $69,440 |
| Median (50th) | $97,450 |
| 75th percentile | $128,940 |
| 90th percentile | $160,710 |
| People employed | 8,780 |
Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is the occupation's national median, not industry-specific.
| Industry | Workers | National median pay |
|---|---|---|
| Professional, Scientific, and Technical Services · Sector | 2,730 | $77,690 |
| Information · Sector | 1,570 | $77,240 |
| Educational Services · Sector | 1,240 | $89,120 |
| Television Broadcasting Stations · National industry | 1,050 | $72,330 |
| Engineering Services · National industry | 210 | — |
| Management of Companies and Enterprises · Sector | 140 | $94,920 |
| Testing Laboratories and Services · National industry | 40 | $90,380 |
| Administrative and Support and Waste Management and Remediation Services · Sector | — | $84,310 |
Industries where this occupation is far more common than in the economy as a whole. The location quotient is how many times more concentrated it is here (a value of 5 means five times its economy-wide share).
| Industry | Concentration | Workers |
|---|---|---|
| Television Broadcasting Stations · National industry | 284.03× | 1,050 |
| Information · Sector | 9.48× | 1,570 |
| Professional, Scientific, and Technical Services · Sector | 4.45× | 2,730 |
| Engineering Services · National industry | 3.19× | 210 |
| Educational Services · Sector | 1.6× | 1,240 |
| Management of Companies and Enterprises · Sector | 0.88× | 140 |
Part of the Public Service & Safety career cluster.
Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.
Options the data surfaces for Atmospheric and Space Scientists — not advice or a forecast. Each is a real cross-link you can follow into the evidence.
Capabilities this work builds that are used across many other occupations.
Occupations O*NET rates as related — the nearby moves on the map.
How people typically prepare for this work.
On the global GenAI exposure gradient this work sits around the 92nd percentile of 427 international occupations.
Atmospheric and Space Scientists show 96th-percentile AI task overlap — and about 700 annual U.S. openings
Atmospheric and Space Scientists show 96th-percentile AI task overlap — and about 700 annual U.S. openings • Atmospheric and Space Scientists rank in the 96th percentile (High band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated. (Eloundou et al. (GPTs are GPTs) + Felten AIOE) • The occupation is projected to see about 700 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI. (BLS Employment Projections 2024–34) • BLS projects employment to be about average (+0.7%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $97,450, across about 8,780 U.S. workers. (BLS OEWS (May 2024)) • Of the AI use actually observed for this work, 51% looks like augmentation (drafting, iterating, checking) rather than hands-off automation — from a Claude.ai usage sample, not a census. (2026-01-15-v4-plus-2025-03-27-v2) Source: Singulariki — "Atmospheric and Space Scientists". https://singulariki.com/roles/role-19-2021-00 Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.
AssetsShare imageMethodology & sourcesPress & newsroomThe newsroom
Every line is built only from figures this page already shows and cites. AI task overlap means what today's AI can attempt — not automation, job loss, or a forecast.
Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.
Data compiled June 2, 2026. Figures are estimates, not advice.
Singulariki. "Atmospheric and Space Scientists." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026. https://singulariki.com/roles/role-19-2021-00
Singulariki. (2026). Atmospheric and Space Scientists. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-19-2021-00
@misc{singulariki-role-19-2021-00,
title = {Atmospheric and Space Scientists},
author = {{Singulariki}},
year = {2026},
note = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026},
url = {https://singulariki.com/roles/role-19-2021-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.