Addressing Deep Learning Model Uncertainty in Long-Range Climate Forecasting with Late Fusion

• URL: http://arxiv.org/abs/2112.05254v1
• Date: Fri, 10 Dec 2021 00:00:09 GMT
• Title: Addressing Deep Learning Model Uncertainty in Long-Range Climate Forecasting with Late Fusion
• Authors: Ken C. L. Wong, Hongzhi Wang, Etienne E. Vos, Bianca Zadrozny, Campbell D. Watson, Tanveer Syeda-Mahmood
• Abstract summary: 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.
• Score: 2.951502707659703
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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