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Addressing Deep Learning Model Uncertainty in Long-Range Climate Forecasting with Late Fusion
Ken C. L. Wong · Hongzhi Wang · Etienne Vos · Bianca Zadrozny · Campbell Watson · Tanveer Syeda-Mahmood
Event URL: https://www.climatechange.ai/papers/neurips2021/4 »

Global warming leads to the increase in frequency and intensity of climate extremes that cause tremendous loss of lives and property. Accurate long-range climate prediction allows more time for preparation and disaster risk management for such extreme events. Although machine learning approaches have shown promising results in long-range climate forecasting, the associated model uncertainties may reduce their reliability. To address this issue, we propose a late fusion approach that systematically combines the predictions from multiple models to reduce the expected errors of the fused results. We also propose a network architecture with the novel denormalization layer to gain the benefits of data normalization without actually normalizing the data. The experimental results on long-range 2m temperature forecasting show that the framework outperforms the 30-year climate normals, and the accuracy can be improved by increasing the number of models.

Author Information

Ken C. L. Wong (IBM Research – Almaden Research Center)
Hongzhi Wang (IBM Almaden Research Center)
Etienne Vos (IBM)
Bianca Zadrozny (IBM Research)
Campbell Watson (IBM Research)

I'm an atmospheric scientist at IBM Research where my research spans climate, weather and water. I was a postdoc at Yale University with Prof. Ron Smith, and completed a PhD at the University of Melbourne with Prof. Todd Lane. Currently leading AI for Climate initiatives with the Future of Climate at IBM Research.

Tanveer Syeda-Mahmood (IBM Almaden Research Center)

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