Understanding Predictability of Daily Southeast U.S. Precipitation Using Explainable Machine Learning

Journal Article · 01 October 2022 · Artificial Intelligence for the Earth Systems

DOI: https://doi.org/10.1175/AIES-D-22-0011.1 · OSTI ID:1900305

Pegion, Kathy  ^[1]; Becker, Emily J. ^[2]; Kirtman, Ben P. ^[2]

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1. a Department of Atmospheric, Oceanic, and Earth Sciences, George Mason University, Fairfax, Virginia, b School of Meteorology, University of Oklahoma, Norman, Oklahoma
2. c Cooperative Institute for Marine and Atmospheric Studies, Rosenstiel School, University of Miami, Miami, Florida

Abstract We investigate the predictability of the sign of daily southeastern U.S. (SEUS) precipitation anomalies associated with simultaneous predictors of large-scale climate variability using machine learning models. Models using index-based climate predictors and gridded fields of large-scale circulation as predictors are utilized. Logistic regression (LR) and fully connected neural networks using indices of climate phenomena as predictors produce neither accurate nor reliable predictions, indicating that the indices themselves are not good predictors. Using gridded fields as predictors, an LR and convolutional neural network (CNN) are more accurate than the index-based models. However, only the CNN can produce reliable predictions that can be used to identify forecasts of opportunity. Using explainable machine learning we identify which variables and grid points of the input fields are most relevant for confident and correct predictions in the CNN. Our results show that the local circulation is most important as represented by maximum relevance of 850-hPa geopotential heights and zonal winds to making skillful, high-probability predictions. Corresponding composite anomalies identify connections with El Niño–Southern Oscillation during winter and the Atlantic multidecadal oscillation and North Atlantic subtropical high during summer.

• Sponsoring Organization: USDOE
• Grant/Contract Number: SC0019433
• OSTI ID: 1900305
• Journal Information: Artificial Intelligence for the Earth Systems, Journal Name: Artificial Intelligence for the Earth Systems Journal Issue: 4 Vol. 1; ISSN 2769-7525
• Publisher: American Meteorological SocietyCopyright Statement
• Country of Publication: United States
• Language: English