Atmospheric and Space Scientists
Life, Physical, and Social Science Occupations- Código O*NET-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.
Exposición a la IA
- Fuente de datos: BLSPublicado: 2026-08
Muy alto· relativa
BajoCuatro bandas relativasMuy altoValor por grupo ocupacional
Escala, base y fuente
Cuatro bandas relativas (Bajo / Moderado / Alto / Muy alto)
831 ocupaciones detalladas de la tabla de proyecciones de empleo de BLS. El valor se asigna por código de la National Employment Matrix (NEM), de modo que las ocupaciones que comparten un código NEM reciben la misma banda
- Fuente de datos: AnthropicPublicado: 2026-03
0.038
0.000Rango de los valores aquí recogidos0.745Escala, base y fuente
Índice de exposición observada, 0–1 tal como se publica
Mapeado sobre tareas de O*NET
Publicado por ocupación SOC 2018; toda ocupación O*NET con el mismo código SOC 2018 recibe este valor
- Fuente de datos: ILOPublicado: 2025
0.54
0.09Rango de los valores aquí recogidos0.70Valor por grupo ocupacional
Escala, base y fuente
Índice de exposición a la IA generativa, 0–1 tal como se publica
Publicado por grupo primario ISCO-08. Vinculado a esta ocupación, total o parcialmente, aplicando tal como se publicaron las tablas de correspondencia de la U.S. Bureau of Labor Statistics (ISCO-08 a SOC 2010, SOC 2010 a SOC 2018)
Qué tipo de cifra publica esta fuente
La categoría de BLS es un rango relativo, no un nivel absoluto, y tampoco es una medición de primera mano: agrupa en cuatro bandas los rangos percentiles de la ocupación en varios estudios publicados. No es una previsión de empleo ni de salarios, no es una probabilidad de adopción y no distingue entre automatización y aumento.
Task-level exposure
Solo tareas expuestas27 tareas evaluadas · 25 tareas con β ≥ 0,5 (92.6%)
β = exposición directa (E1) + 0,5 × exposición con herramientas disponibles (E2), según la definición del repositorio de origen.
- Unidad de origen: tareas de O*NET 27.2 → código de ocupación de O*NET 31.0
- Todas las tareas puntuadas figuran en la lista de tareas de O*NET 31.0.
- Fuente
- OpenAI "GPTs are GPTs" exposure rubric
- Versión
- gh-main-0471612
- Licencia
- MIT License, Copyright (c) 2024 OpenAI
| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
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.19-2021.00 | 0.038543.5 | —0 |
Gather data from sources such as surface or upper air stations, satellites, weather bureaus, or radar for use in meteorological reports or forecasts.19-2021.00 | 0.033037.3 | 0.010486.0 |
Conduct meteorological research into the processes or determinants of atmospheric phenomena, weather, or climate.19-2021.00 | 0.00606.8 | —0 |
Design or develop new equipment or methods for meteorological data collection, remote sensing, or related applications.19-2021.00 | 0.00576.5 | 0.001714.0 |
Prepare weather reports or maps for analysis, distribution, or use in weather broadcasts, using computer graphics.19-2021.00 | 0.00525.9 | —0 |
| Not observed on any surface — 19 task(s) — These tasks have no row in the source for this release. The 0 in the share row is a display-stage composition ratio; absence is what the — in the raw row states. | ||
Broadcast weather conditions, forecasts, or severe weather warnings to the public via television, radio, or the Internet or provide this information to the news media. | —0 | —0 |
Prepare forecasts or briefings to meet the needs of industry, business, government, or other groups. | —0 | —0 |
Measure wind, temperature, and humidity in the upper atmosphere, using weather balloons. | —0 | —0 |
Direct forecasting services at weather stations or at radio or television broadcasting facilities. | —0 | —0 |
Conduct numerical simulations of climate conditions to understand and predict global or regional weather patterns. | —0 | —0 |
Analyze climate data sets, using techniques such as geophysical fluid dynamics, data assimilation, or numerical modeling. | —0 | —0 |
Formulate predictions by interpreting environmental data, such as meteorological, atmospheric, oceanic, paleoclimate, climate, or related information. | —0 | —0 |
Perform managerial duties, such as creating work schedules, creating or implementing staff training, matching staff expertise to situations, or analyzing performance of offices. | —0 | —0 |
Consult with other offices, agencies, professionals, or researchers regarding the use and interpretation of climatological information for weather predictions and warnings. | —0 | —0 |
Analyze historical climate information, such as precipitation or temperature records, to help predict future weather or climate trends. | —0 | —0 |
Apply meteorological knowledge to issues such as global warming, pollution control, or ozone depletion. | —0 | —0 |
Develop or use mathematical or computer models for weather forecasting. | —0 | —0 |
Prepare scientific atmospheric or climate reports, articles, or texts. | —0 | —0 |
Research the impact of industrial projects or pollution on climate, air quality, or weather phenomena. | —0 | —0 |
Collect air samples from planes or ships over land or sea to study atmospheric composition. | —0 | —0 |
Conduct wind assessment, integration, or validation studies. | —0 | —0 |
Create visualizations to illustrate historical or future changes in the Earth's climate, using paleoclimate or climate geographic information systems (GIS) databases. | —0 | —0 |
Estimate or predict the effects of global warming over time for specific geographic regions. | —0 | —0 |
Teach college-level courses on topics such as atmospheric and space science, meteorology, or global climate change. | —0 | —0 |
Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.
| Task | βE1 + 0.5 × E2 |
|---|---|
Develop or use mathematical or computer models for weather forecasting.O*NET Task ID 20208 | 1.0 |
Prepare scientific atmospheric or climate reports, articles, or texts.O*NET Task ID 20211 | 1.0 |
Speak to the public to discuss weather topics or answer questions.O*NET Task ID 21103 | 1.0 |
Develop computer programs to collect meteorological data or to present meteorological information.O*NET Task ID 21104 | 1.0 |
Broadcast weather conditions, forecasts, or severe weather warnings to the public via television, radio, or the Internet or provide this information to the news media.O*NET Task ID 9068 | 0.5 |
Gather data from sources such as surface or upper air stations, satellites, weather bureaus, or radar for use in meteorological reports or forecasts.O*NET Task ID 9069 | 0.5 |
Prepare forecasts or briefings to meet the needs of industry, business, government, or other groups.O*NET Task ID 9070 | 0.5 |
Direct forecasting services at weather stations or at radio or television broadcasting facilities.O*NET Task ID 9076 | 0.5 |
Conduct numerical simulations of climate conditions to understand and predict global or regional weather patterns.O*NET Task ID 9079 | 0.5 |
Design or develop new equipment or methods for meteorological data collection, remote sensing, or related applications.O*NET Task ID 9083 | 0.5 |
Measure wind, temperature, and humidity in the upper atmosphere, using weather balloons.O*NET Task ID 9074 | 0.0 |
Collect air samples from planes or ships over land or sea to study atmospheric composition.O*NET Task ID 9078 | 0.0 |
β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.
Data sources & licenses — O*NET®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page
Todas las tareas de O*NET 31.0 (27)
Todas las tareas de O*NET 31.0 de esta ocupación (en inglés, tal como se publicaron; tipo de tarea principal o complementaria). Los valores de exposición no figuran en esta lista; están en la tabla de arriba, junto a cada tarea con la redacción de la versión de O*NET que usó cada fuente.
| Tarea | Tipo |
|---|---|
| Broadcast weather conditions, forecasts, or severe weather warnings to the public via television, radio, or the Internet or provide this information to the news media. | Principal |
| Gather data from sources such as surface or upper air stations, satellites, weather bureaus, or radar for use in meteorological reports or forecasts. | Principal |
| Prepare forecasts or briefings to meet the needs of industry, business, government, or other groups. | Principal |
| Measure wind, temperature, and humidity in the upper atmosphere, using weather balloons. | Principal |
| Analyze climate data sets, using techniques such as geophysical fluid dynamics, data assimilation, or numerical modeling. | Principal |
| Formulate predictions by interpreting environmental data, such as meteorological, atmospheric, oceanic, paleoclimate, climate, or related information. | Principal |
| Perform managerial duties, such as creating work schedules, creating or implementing staff training, matching staff expertise to situations, or analyzing performance of offices. | Principal |
| Consult with other offices, agencies, professionals, or researchers regarding the use and interpretation of climatological information for weather predictions and warnings. | Principal |
| Analyze historical climate information, such as precipitation or temperature records, to help predict future weather or climate trends. | Principal |
| Prepare weather reports or maps for analysis, distribution, or use in weather broadcasts, using computer graphics. | Principal |
| Apply meteorological knowledge to issues such as global warming, pollution control, or ozone depletion. | Principal |
| Develop or use mathematical or computer models for weather forecasting. | Principal |
| 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. | Principal |
| Conduct meteorological research into the processes or determinants of atmospheric phenomena, weather, or climate. | Principal |
| Prepare scientific atmospheric or climate reports, articles, or texts. | Principal |
| Speak to the public to discuss weather topics or answer questions. | Principal |
| Develop computer programs to collect meteorological data or to present meteorological information. | Principal |
| Develop and deliver training on weather topics. | Principal |
| Direct forecasting services at weather stations or at radio or television broadcasting facilities. | Complementaria |
| Collect air samples from planes or ships over land or sea to study atmospheric composition. | Complementaria |
| Conduct numerical simulations of climate conditions to understand and predict global or regional weather patterns. | Complementaria |
| Design or develop new equipment or methods for meteorological data collection, remote sensing, or related applications. | Complementaria |
| Conduct wind assessment, integration, or validation studies. | Complementaria |
| Create visualizations to illustrate historical or future changes in the Earth's climate, using paleoclimate or climate geographic information systems (GIS) databases. | Complementaria |
| Estimate or predict the effects of global warming over time for specific geographic regions. | Complementaria |
| Teach college-level courses on topics such as atmospheric and space science, meteorology, or global climate change. | Complementaria |
| Research the impact of industrial projects or pollution on climate, air quality, or weather phenomena. | Complementaria |
Índice de exposición a robots
Anthropic, «What work can robots do?» (2026)
0,03/ 3
Cada tarea física entre las tareas O*NET 29.3 de esta ocupación se califica de 0 a 3 según el entorno en el que robots ya implantados, vendidos o demostrados podrían realizarla (0 ninguno · 1 entornos diseñados para robots, como una celda de máquinas vallada o una línea de embotellado · 2 instalaciones estructuradas, como una farmacia hospitalaria o un puerto de contenedores · 3 entornos no estructurados, como un hogar particular o una obra de construcción), y las calificaciones se promedian según la proporción del tiempo de trabajo. Anthropic estimó las calificaciones y las proporciones de tiempo con Claude Opus 5 mediante búsqueda web (situación en 2026). El índice no mide la adopción real ni el coste, y el 0 también incluye ocupaciones sin tareas físicas. Es una medida distinta de las cifras de exposición a la IA de este sitio y no puede sumarse a ellas ni compararse con ellas.
Calificación de cada tarea física (enunciados de las tareas tal como figuran en O*NET 29.3)
- 2 instalaciones estructuradasMeasure wind, temperature, and humidity in the upper atmosphere, using weather balloons.
- 2 instalaciones estructuradasCollect air samples from planes or ships over land or sea to study atmospheric composition.
Información ocupacional
Fuentes de datos y licencias de esta página, y avisos de modificación y traducción de AI Changing Work: Créditos y fuentes