Atmospheric and Space Scientists

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.

AI: Fully automatable - AI systems can fully automate generation and dissemination of weather bulletins (scripts, synthesized speech/video, posting to web/radio streams) and supply media-ready content in real time.

Prepare weather reports or maps for analysis, distribution, or use in weather broadcasts, using computer graphics.

AI: Fully automatable - Creating weather maps and broadcast graphics from model outputs is already routinely automated with software pipelines and AI can produce high-quality visualizations for distribution.

Gather data from sources such as surface or upper air stations, satellites, weather bureaus, or radar for use in meteorological reports or forecasts.

AI: Fully automatable - Data ingestion from sensors, satellites, radar, and bureaus is readily automated with end‑to‑end pipelines that AI can manage and quality‑control in real time.

Conduct numerical simulations of climate conditions to understand and predict global or regional weather patterns.

AI: Fully automatable - Running large numerical climate and regional simulation experiments and post‑processing results is well within current automated HPC workflows and AI tools for configuration, execution, and analysis.

Formulate predictions by interpreting environmental data, such as meteorological, atmospheric, oceanic, paleoclimate, climate, or related information.

AI: Fully automatable - By 2025, AI systems combined with numerical models can ingest environmental datasets and generate operational forecasts and interpreted predictions with human-level accuracy for many applications.

Prepare scientific atmospheric or climate reports, articles, or texts.

AI: Fully automatable - Modern LLMs and automated document-generation pipelines can produce scientific atmospheric or climate reports, drafts, and summaries suitable for review and publication.

Analyze historical climate information, such as precipitation or temperature records, to help predict future weather or climate trends.

AI: Fully automatable - AI/ML tools and statistical models can robustly analyze historical climate records and produce predictive insights and trend projections at scale.

Analyze climate data sets, using techniques such as geophysical fluid dynamics, data assimilation, or numerical modeling.

AI: Fully automatable - AI can run, optimize, and interpret complex workflows—data assimilation, numerical modeling, and fluid-dynamics analyses—given appropriate models and computing resources.

Apply meteorological knowledge to issues such as global warming, pollution control, or ozone depletion.

AI: Fully automatable - AI can apply meteorological knowledge to analyze and model issues like climate change, pollution control, and ozone impacts and produce actionable recommendations.

Research the impact of industrial projects or pollution on climate, air quality, or weather phenomena.

AI: Fully automatable - AI can synthesize emissions data, dispersion modeling, and climate/air-quality simulations to assess industrial and pollution impacts and generate research-quality assessments.

Create visualizations to illustrate historical or future changes in the Earth's climate, using paleoclimate or climate geographic information systems (GIS) databases.

AI: Fully automatable - AI can ingest paleoclimate and GIS databases, perform analyses and downscaling, and produce maps, graphs, and animations illustrating historical and projected climate changes.

Estimate or predict the effects of global warming over time for specific geographic regions.

AI: Fully automatable - AI can run climate models, perform regional downscaling, and produce probabilistic projections of warming effects for specific regions, enabling full automation of such estimates.

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.

AI: Partial - AI and numerical models can perform the bulk of data interpretation and forecasting, but human forecasters still provide critical judgment for complex or unusual situations and verification.

Develop or use mathematical or computer models for weather forecasting.

AI: Partial - AI can develop, tune, and run forecasting models and assist heavily in model building, but fully autonomous creation and validation of new operational models without expert oversight remains limited.

Prepare forecasts or briefings to meet the needs of industry, business, government, or other groups.

AI: Partial - AI can produce tailored forecasts and briefings for specific sectors and audiences, but human specialists are often required to adapt messaging, interpret tradeoffs, and ensure stakeholder coordination in critical contexts.

Measure wind, temperature, and humidity in the upper atmosphere, using weather balloons.

AI: Partial - While sensors on weather balloons automatically record upper‑air measurements and AI can process the data, the physical launching, recovery and some field operations remain at least partially manual or require specialized operational oversight.

Direct forecasting services at weather stations or at radio or television broadcasting facilities.

AI: Partial - AI can automate many operational forecasting tasks and provide decision support, but directing services (leadership, accountability, stakeholder relations) requires human authority and judgment.

Perform managerial duties, such as creating work schedules, creating or implementing staff training, matching staff expertise to situations, or analyzing performance of offices.

AI: Partial - AI can automate schedule creation, training materials, and performance analytics but cannot fully replace human judgment and leadership responsibilities in managerial duties.

Consult with other offices, agencies, professionals, or researchers regarding the use and interpretation of climatological information for weather predictions and warnings.

AI: Partial - AI can provide technical interpretations and briefing materials for interagency use, but authoritative consultation and cross-organizational coordination still require human experts.

Conduct meteorological research into the processes or determinants of atmospheric phenomena, weather, or climate.

AI: Partial - AI accelerates data analysis, model experimentation, and hypothesis generation, but original meteorological research still requires human-driven experimental design, fieldwork, and scientific judgment.

Design or develop new equipment or methods for meteorological data collection, remote sensing, or related applications.

AI: Partial - AI can propose and simulate new sensor designs and collection methods, but actual hardware development, prototyping, and field validation remain human-led tasks.

Teach college-level courses on topics such as atmospheric and space science, meteorology, or global climate change.

AI: Partial - AI can generate lectures, assignments, and assessments and simulate teaching, but cannot fully replace in-person mentorship, lab supervision, and classroom dynamics.

Conduct wind assessment, integration, or validation studies.

AI: Partial - AI can run wind models and analyze data for assessments and validation, but cannot by itself perform field measurements, engineering integration, and stakeholder coordination required for complete studies.

Collect air samples from planes or ships over land or sea to study atmospheric composition.

AI: Not automatable - Collecting air samples from planes or ships requires physical field operations and specialized on-site equipment that AI alone cannot perform.