|Title||Global assessment of atmospheric river prediction skill|
|Publication Type||Journal Article|
|Year of Publication||2018|
|Authors||DeFlorio M.J, Waliser D.E, Guan B., Lavers D.A, Ralph FM, Vitart F.|
|Journal||Journal of Hydrometeorology|
|Type of Article||Article|
|Keywords||american teleconnection pattern; climate-change; el-nino; extreme precipitation events; forecast skill; Meteorology & Atmospheric Sciences; north-atlantic oscillation; sea-surface temperature; south-america; united-states; west-coast|
Atmospheric rivers (ARs) are global phenomena that transport water vapor horizontally and are associated with hydrological extremes. In this study, the Atmospheric River Skill (ATRISK) algorithm is introduced, which quantifies AR prediction skill in an object-based framework using Subseasonal to Seasonal (S2S) Project global hindcast data from the European Centre for Medium-Range Weather Forecasts (ECMWF) model. The dependence of AR forecast skill is globally characterized by season, lead time, and distance between observed and forecasted ARs. Mean values of daily AR prediction skill saturate around 7-10 days, and seasonal variations are highest over the Northern Hemispheric ocean basins, where AR prediction skill increases by 15%-20% at a 7-day lead during boreal winter relative to boreal summer. AR hit and false alarm rates are explicitly considered using relative operating characteristic (ROC) curves. This analysis reveals that AR forecast utility increases at 10-day lead over the North Pacific/western U.S. region during positive El Nino-Southern Oscillation (ENSO) conditions and at 7-and 10-day leads over the North Atlantic/U.K. region during negative Arctic Oscillation (AO) conditions and decreases at a 10-day lead over the North Pacific/western U.S. region during negative Pacific-North America (PNA) teleconnection conditions. Exceptionally large increases in AR forecast utility are found over the North Pacific/western United States at a 10-day lead during El Nino + positive PNA conditions and over the North Atlantic/United Kingdom at a 7-day lead during La Nina + negative PNA conditions. These results represent the first global assessment of AR prediction skill and highlight climate variability conditions that modulate regional AR forecast skill.
|Short Title||J. Hydrometeorol.|
This study builds on recent advances in objective detection of atmospheric rivers that can be applied to both observations (GW2015) and global weather and climate models (Guan and Waliser 2017) and the availability of subseasonal hindcasts from operational models (Vitart et al. 2017) to quantify global prediction skill of ARs using two decades of ECMWF hindcast data at lead times ranging from 1 to 14 days. AR prediction skill is first quantified by diagnosing the ability of the ECMWF model to predict an observed AR as a function of lead time and distance threshold between the centroid of an observed and forecasted AR, averaged over all hindcasts. The average percentage of ECMWF ensemble members that skillfully predict an AR shows highest variability during boreal winter in the North Pacific/western United States. Southern Hemispheric regions exhibit little seasonal dependence in AR forecast skill. Over the North Pacific/western U.S. region, the lead time at which AR hit percentage equals 50% increases by 2.5 days during NDJFM relative to MJJAS using a 1000-km threshold, and the AR hit percentage at a 7-day lead time increases by 15%–20% during NDJFM relative to MJJAS using a 1000-km distance threshold.