Subseasonal Prediction of Impactful California Winter Weather in a Hybrid Dynamical‐Statistical Framework

Autor: Kristen Guirguis, Alexander Gershunov, Benjamin J. Hatchett, Michael J. DeFlorio, Aneesh C. Subramanian, Rachel Clemesha, Luca Delle Monache, F. Martin Ralph
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Geophysical Research Letters, Vol 50, Iss 23, Pp n/a-n/a (2023)
Druh dokumentu: article
ISSN: 1944-8007
0094-8276
DOI: 10.1029/2023GL105360
Popis: Abstract 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 impactful winter weather in California, including ARs, SAWs and heat extremes. The hybrid approach combines dynamical model and historical information to forecast probabilities of impactful weather outcomes at weeks 1–4 lead. This methodology uses dynamical model information considered most reliable, that is, planetary/synoptic‐scale atmospheric circulation, filters for dynamical model error/uncertainty at longer lead times and 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.
Databáze: Directory of Open Access Journals
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