Fusing Climate Data Products using a Spatially Varying Autoencoder
This work addresses the need for interpretable data fusion in climate science, though it appears incremental as it builds on existing autoencoder methods with spatial and Bayesian enhancements.
The researchers tackled the problem of unidentifiable and uninterpretable autoencoders by developing a spatially varying Bayesian autoencoder to fuse climate data products, demonstrating its utility by combining multiple precipitation datasets in High Mountain Asia.
Autoencoders are powerful machine learning models used to compress information from multiple data sources. However, autoencoders, like all artificial neural networks, are often unidentifiable and uninterpretable. This research focuses on creating an identifiable and interpretable autoencoder that can be used to meld and combine climate data products. The proposed autoencoder utilizes a Bayesian statistical framework, allowing for probabilistic interpretations while also varying spatially to capture useful spatial patterns across the various data products. Constraints are placed on the autoencoder as it learns patterns in the data, creating an interpretable consensus that includes the important features from each input. We demonstrate the utility of the autoencoder by combining information from multiple precipitation products in High Mountain Asia.