|Title||The gauging and modeling of rivers in the sky|
|Publication Type||Journal Article|
|Year of Publication||2018|
|Authors||Lavers D.A, Rodwell M.J, Richardson D.S, Ralph FM, Doyle J.D, Reynolds C.A, Tallapragada V., Pappenberger F.|
|Journal||Geophysical Research Letters|
|Type of Article||Article|
|Keywords||atmospheric rivers; dropsonde observations; Geology; moisture; pacific-ocean; prediction; satellite; water-vapor transport; winter|
Atmospheric rivers (ARs) are responsible for most of the horizontal water vapor flux outside of the tropics and can cause extreme precipitation and affect the atmospheric dynamics and predictability. For their impacts to be skillfully predicted, it is essential for weather forecasting systems to accurately represent AR characteristics. Using the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System and dropsonde observations from the 2018 AR Reconnaissance field campaign over the Northeast Pacific Ocean, it is shown that the AR structure is modeled well but that short-range water vapor flux forecasts have a root-mean-square error of 60.0 kgm(-1) s(-1) (21.9% of mean observed flux). These errors are most related to uncertainties in the winds near the top of the planetary boundary layer. The findings identify a potential barrier in the prediction of high-impact weather and suggest an area where research should be focused to improve atmospheric forecast systems. Plain Language Summary Atmospheric rivers (ARs) are responsible for most of the horizontal transport of water vapor outside of the tropics and can cause extreme precipitation and affect the atmospheric circulation. In this study, we evaluate the ability of a state-of-the-science weather forecasting system to model ARs by using unique atmospheric observations from the 2018 AR Reconnaissance field campaign. Results show that while the AR structure is modeled well, there can be large errors in the water vapor transport which are most related to uncertainties in the low-level winds. These findings identify a potential barrier in the prediction of high-impact weather.