Multi-Model Assessment of Subseasonal-to-Seasonal Atmospheric River Prediction Skill

Atmospheric rivers (ARs) are global phenomena that are characterized by long, narrow plumes of water vapor transport. They are most often observed in the midlatitudes near climatologically active storm track regions. Because of their frequent association with floods, landslides, and other hydrological impacts on society, there is significant incentive at the intersection of academic research, water management, and policymaking to understand the skill with which state-of-the-art operational weather models can predict ARs weeks-to-months in advance. We use the newly assembled Subseasonal-to-Seasonal (S2S) database, which includes extensive hindcast records of eleven operational weather models, to assess global prediction skill of atmospheric rivers on S2S timescales. We develop a metric to assess AR skill that is suitable for S2S timescales by counting the total number of AR days which occur over each model and observational grid cell during a 2-week time window. This "2-week AR occurrence" metric is suitable for S2S prediction skill assessment because it does not consider discrete hourly or daily AR objects, but rather a smoothed representation of AR occurrence over a longer period of time. Our results indicate that several of the S2S models, especially the ECMWF model, show useful prediction skill in the 2-week forecast window, with significant interannual variation in some regions. We also present results from an experimental forecast of S2S AR prediction skill using the ECMWF and NCEP models.