Preparing a diverse new generation of scientists who can use artificial intelligence and data science to better understand and predict geoscience phenomena requires revamped training.
Machine learning is gaining popularity across scientific and technical fields, but it’s often not clear to researchers, especially young scientists, how they can apply these methods in their work.
Machine learning and signal processing methods offer significant benefits to the geosciences, but realizing this potential will require closer engagement among different research communities.
As weather and climate models grow larger and more data intensive, the amount of energy needed to run them continues to increase. Are researchers doing enough to minimize the carbon footprint of their computing?
Developing trustworthy artificial intelligence for weather and ocean forecasting, as well as for long-term environmental sustainability, requires integrating collaborative efforts from many sources.
Recent advances in machine learning hold great potential for converting a deluge of data into weather forecasts that are fast, accurate, and detailed.
A step-by-step cartoon guide to efficient, effective collaboration between Earth scientists and data scientists.
Globally relevant and locally devastating, hailstorms produce significant societal impacts; despite this, our understanding of hailstorms and our ability to predict them is still limited.
The search for life, developing flagship telescopes, partnering with the private sector, and maintaining Earth science programs should be top priorities for the space agency, say witnesses.