|Title||Experimental subseasonal-to-seasonal (S2S) forecasting of atmospheric rivers over the western United States|
|Publication Type||Journal Article|
|Year of Publication||2019|
|Authors||DeFlorio M.J, Waliser D.E, Ralph FM, Guan B., Goodman A., Gibson P.B, Asharaf S., L. Monache D, Zhang Z.H, Subramanian AC, Vitart F., Lin H., Kumar A.|
|Type of Article||Article|
|Keywords||area; coast; impacts; intensity; Meteorology & Atmospheric Sciences; MJO; precipitation; prediction; satellite; scale; strength|
A multimodel evaluation of subseasonal-to-seasonal (S2S) hindcast skill of atmospheric rivers (ARs) out to 4-week lead over the western United States is presented for three operational hindcast systems: European Centre for Medium-Range Weather Forecasts (ECMWF; Europe), National Centers for Environmental Prediction (NCEP; U.S.), and Environment and Canada Climate Change (ECCC; Canada). Ensemble mean biases and Brier Skill Scores are examined for no, moderate, and high levels of AR activity (0, 1-2, and 3-7 AR days/week, respectively). All hindcast systems are more skillful in predicting no and high AR activity relative to moderate activity. There are isolated regions of skill at week-3 over 150-125 degrees W, 25-35 degrees N for the no and high AR activity levels, with larger magnitude and spatial extent of the skill in ECMWF and ECCC compared to NCEP. The spatial pattern of this skill suggests that for high AR activity, a southwest-to-northeast orientation is more predictable at subseasonal lead times than other orientations, and for no AR activity, more skill exists in the subtropical North Pacific, upstream of central and southern California. AR hindcast skill along the western U.S. is most strongly increased in hindcasts initialized during MaddenJulian Oscillation (MJO) Phases 1 and 8, and hindcast skill is substantially decreased over California in hindcasts initialized during MJO Phase 4. Skill modulations in the ECMWF hindcast system conditioned on El Nino-Southern Oscillation phase are weaker than those conditioned on particular MJO phases. This work provides hindcast skill benchmarks and uncertainty quantification for experimental real-time forecasts of AR activity during winters 2019-2021 as part of the S2S Prediction Project Real-time Pilot Initiative in collaboration with the California Department of Water Resources.