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Subseasonal prediction of impactful California weather in a hybrid dynamical-statistical framework
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  • Kristen Guirguis,
  • Alexander Gershunov,
  • Benjamin J Hatchett,
  • Michael J DeFlorio,
  • Aneesh Subramanian,
  • Rachel E. S. Clemesha,
  • Luca Delle Monache,
  • F. Martin Ralph
Kristen Guirguis
Scripps Institution of Oceanography, Univ. California, San Diego

Corresponding Author:kguirguis@ucsd.edu

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Alexander Gershunov
Scripps Institution of Oceanography, Univ. California, San Diego
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Benjamin J Hatchett
Desert Research Institute
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Michael J DeFlorio
Scripps Institution of Oceanography
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Aneesh Subramanian
University of Colorado
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Rachel E. S. Clemesha
Scripps Institution of Oceanography, University of California, San Diego
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Luca Delle Monache
University of California San Diego
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F. Martin Ralph
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Atmospheric rivers (ARs) and Santa Ana winds (SAWs) are impactful weather events for California communities. Emergency planning efforts and resource management would benefit from extending lead times of skillful prediction for these and other types of extreme weather patterns. Here we describe a methodology for subseasonal prediction of extreme winter weather in California, including ARs, SAWs and temperature extremes. The hybrid approach combines dynamical model and historical information to forecast probabilities of impactful weather outcomes at weeks 1-4 lead. This methodology (i) uses dynamical model information considered most reliable, i.e., planetary/synoptic-scale atmospheric circulation, (ii) filters for dynamical model error/uncertainty at longer lead times, and (iii) increases the sample of likely outcomes by utilizing the full historical record instead of a more limited suite of dynamical forecast model ensemble members. We demonstrate skill above climatology at subseasonal timescales, highlighting potential for use in water, health, land, and fire management decision support.
17 Jul 2023Submitted to ESS Open Archive
20 Jul 2023Published in ESS Open Archive