Using AI to Study Weather & Climate
from Weather Geeks
Guest: Campbell Watson, Senior Research Scientist at IBM Research As artificial intelligence, or AI, continues to become more pervasive in our technology, it’s only natural to wonder what it means for meteorology and climatology. Believe it or not, AI is already revolutionizing how we develop models in the Earth and Space sciences. Joining us today is Campbell Watson, a Senior Research Scientist at IBM Research, to discuss how we are creating these AI models, and the opportunities and advancements we hope to learn from using them. Chapters 00:00 Introduction to AI in Meteorology and Climatology 01:35 Campbell Watson's Background and Interest in Weather 04:18 The Use of AI in Weather Forecasting 05:24 The Evolution of AI in Weather Forecasting 06:26 Understanding AI at its Core 08:09 Difference Between AI and Conventional Weather Model Forecasting 10:44 The Difference Between AI and Conventional Weather Model Forecasting 13:31 AI Models Learning from Weather Models 20:30 Foundation Models and Unsupervised Learning 25:12 Verification of AI in Weather Forecasting 27:57 The Robustness of AI Models 28:24 Learning from Observations 29:23 AI Models Learning from Observations 30:09 The Importance of Observations 30:38 Comparing Deep Learning and AI with Traditional Weather Forecasting Models 35:38 Trustworthiness in AI Forecasting 36:12 Adopting AI in Weather Forecasting 41:46 Nowcasting with AI 44:03 AI in Climate Forecasting 47:38 The Future of AI in Weather Forecasting
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What this episode covers
In this conversation, Jen Carfagno and Campbell Watson discuss the use of artificial intelligence (AI) in meteorology and climatology. They explore Campbell's background and interest in weather, as well as his work at IBM Research. They delve into the difference between AI and conventional weather model forecasting, and how AI models learn from weather models. They also discuss the use of AI in weather forecasting, including the Graphcast model. The conversation concludes with a discussion on foundation models and the verification of AI in weather forecasting. In this conversation, Campbell Watson discusses the robustness of AI models and how they can improve weather forecasting. He explains the importance of learning from observations and how AI models can learn from historical data to make more accurate forecasts. Watson also discusses the challenges of integrating AI models with traditional weather forecasting models and the potential for AI to improve nowcasting and climate forecasting. He emphasizes the need for trustworthiness in AI forecasting and the importance of effectively communicating probability in weather forecasts. Overall, Watson is excited about the future of AI in weather forecasting and its potential to make forecasts more accurate and easier for forecasters.
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