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Publication Date
17 July 2023

Assessing Tropical Pacific-induced Predictability of Southern California Precipitation Using a Novel Multi-input Multi-output Autoencoder



We construct a novel Multi-Input Multi-Output Autoencoder-decoder (MIMO-AE) to capture the non-linear relationship of Southern California precipitation and tropical Pacific Ocean sea surface temperature. The MIMO-AE is trained on both monthly TP-SST and SC-PRECIP anomalies simultaneously. The co-variability of the two fields in the MIMO-AE shared nonlinear latent space can be condensed into an index, termed the MIMO-AE index. We use a transfer learning approach to train a MIMO-AE on the combined dataset of 100 years of output from a historical simulation with the Energy Exascale Earth Systems Model version 1 and a segment of observational data. We further use Long Short-Term Memory networks to assess sub-seasonal predictability of SC-PRECIP using the MIMO-AE index. We find that the MIMO-AE index provides enhanced predictability of SC-PRECIP for a lead-time of up-to four months as compared to Niño 3.4 index and the El Niño Southern Oscillation Longitudinal Index.

Passarella, Linsey S, and Salil Mahajan. 2023. “Assessing Tropical Pacific-Induced Predictability Of Southern California Precipitation Using A Novel Multi-Input Multi-Output Autoencoder”. Artificial Intelligence For The Earth Systems. American Meteorological Society, 1-30. doi:10.1175/aies-d-23-0003.1.
Funding Program Area(s)
Additional Resources:
ALCC (ASCR Leadership Computing Challenge)
NERSC (National Energy Research Scientific Computing Center)