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Pegion, K., Becker, E. J., & Kirtman, B. P. (2022). Understanding Predictability of Daily Southeast US Precipitation Using Explainable Machine Learning. Artificial Intelligence for the Earth Systems, 1, e220011.
https://doi.org/10.1175/AIES-D-22-0011.1

has been cited by the following article:

• TITLE: Precipitation Cycles Relative to Storm Tracks, ENSO and PDO, and Drought—Continental Interior Central Western USA

  AUTHORS: Alan L. Mayo

  KEYWORDS: Precipitation Cycle, Meteoric Drought, Hydrologic Drought, Snow Drought, Climate Change

  JOURNAL NAME: American Journal of Climate Change, Vol.12 No.3, August 18, 2023

  ABSTRACT: Seventy-two years of central western United States precipitation data have been analyzed for storms originating 1000 to 3000 km away from four ocean moisture sources: Arctic, North Pacific, South Pacific, and Gulfs of California and Mexico. Precipitation trends were evaluated relative to precipitation phase, precipitation flux, storm track trajectory, and the sea surface temperature (SST) indices Oceanic Niño Index (ONI), and the Pacific Decadal Oscillation (PDO. The lack of correlation between SST indices with precipitation flux was evaluated. The relationships of meteorological, hydrological and snow droughts were evaluated relative to each other, to the climate change-induced temporal shifts in the timing of mountain snowpack decay, and the timing when North Pacific storm tracks shift from crossing to circumventing the Sierra Nevada Range.