Fusing Climate Data Products using a Spatially Varying Autoencoder
Jacob A. Johnson Matthew J. Heaton William F. Christensen Lynsie R. Warr Summer B. Rupper
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.