Atmospheric and Space Scientists

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.

The occupation "Atmospheric and Space Scientists" has an automation risk of 54.7%, reflecting a moderate likelihood that certain aspects of the role could be automated in the near future. The base risk, measured at 55.6%, suggests that more than half of the tasks involved are susceptible to automation, primarily due to recent advancements in data analytics, artificial intelligence, and computer modeling. These technologies are increasingly capable of handling routine and repeatable tasks, especially those centered around data analysis and model-driven forecasting. The rate is not higher, however, because the field requires a mix of computational proficiency and scientific intuition, which are not easily replicated by machines. The most automatable tasks for Atmospheric and Space Scientists involve highly structured and data-intensive processes. For instance, developing or using mathematical or computer models for weather forecasting is increasingly managed by advanced algorithms and automated systems; once models are designed, much of the data input, processing, and short-term prediction can be handled by software. Similarly, interpreting data, reports, maps, and charts to predict weather conditions—using current computer models and deep scientific knowledge—lends itself well to automation given the improvements in pattern-recognition by AI systems. Conducting meteorological research into atmospheric phenomena also involves significant data processing and simulation work, which is conducive to automation by high-powered computers. Despite these factors, some key tasks remain resistant to automation due to their reliance on creative problem-solving and original insight. Notably, creating visualizations to illustrate climate changes using paleoclimate or GIS databases often requires innovative approaches to data storytelling and communication, which current AI systems handle poorly. Estimating or predicting the effects of global warming for specific geographic regions involves synthesizing novel data and applying expert judgment beyond what algorithms alone can currently achieve. Conducting numerical simulations of climate conditions, especially when the parameters involve uncertainty or incomplete data, necessitates creative hypothesis-testing and adjustment. These resistant tasks are supported by bottleneck skills such as originality, rated at 3.0% and 3.1%, underscoring the importance of innovative thinking and unique human contributions in this scientific field.