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The Gauging and Modeling of Rivers in the Sky
Author(s) -
Lavers David A.,
Rodwell Mark J.,
Richardson David S.,
Ralph F. Martin,
Doyle James D.,
Reynolds Carolyn A.,
Tallapragada Vijay,
Pappenberger Florian
Publication year - 2018
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2018gl079019
Subject(s) - predictability , environmental science , climatology , precipitation , quantitative precipitation forecast , dropsonde , meteorology , flux (metallurgy) , atmospheric research , water vapor , atmospheric sciences , data assimilation , weather forecasting , global forecast system , numerical weather prediction , atmospheric model , range (aeronautics) , forecast skill , tropical cyclone , geology , geography , physics , materials science , quantum mechanics , metallurgy , composite material
Abstract 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.