Purely Data-Driven Approaches to Weather Prediction: Promise and Perils | Suman Ravuri | DISCOVERY
8The use of machine learning (ML) models for weather prediction has emerged as a popular area of research. The promise of these models — whether in conjunction with more traditional Numerical Weather Prediction (NWP), or on its own — is that they allow for more accurate predictions of the weather at significantly reduced computational cost.
This AI for Good Discovery featuring Suman Ravuri, Staff Research Scientist at @Google_DeepMind , discusses both the promise and perils of using a data-driven (and more specifically deep learning) only approach. It highlights a recent project with the Met Office on precipitation nowcasting as a case study. While the study found that we could create a deep learning model that was significantly preferred by Met Office meteorologists and performed well on objective measures of performance, we also discovered many ways in which deep learning systems can perform well on objective measures of performance without improving decision-making value. This talk discusses some reasons why this failure mode occurs, while also advocating for better verification of purely data-driven models. Although the discussion focuses on very-short-term prediction, we believe that many of these lessons are also applicable to longer-term forecasts using machine learning for weather prediction.
🎙 Speaker:
Suman Ravuri
Staff Research Scientist
DeepMind
🎙 Moderators:
Duncan Watson-Parris
Postdoctoral Research Associate
University of Oxford
Philip Stier
Head of Atmospheric, Oceanic and Planetary Physics
University of Oxford
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