Subseasonal prediction of impactful California weather in a hybrid
dynamical-statistical framework
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
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.