Affiliation:
1. Scripps Institution of Oceanography University of California San Diego La Jolla CA USA
2. Desert Research Institute Reno NV USA
3. University of Colorado Boulder Boulder CO USA
Abstract
AbstractAtmospheric 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.
Funder
National Science Foundation
National Oceanic and Atmospheric Administration
Southwest Climate Adaptation Science Center
Publisher
American Geophysical Union (AGU)
Subject
General Earth and Planetary Sciences,Geophysics