Comparison of Global Atmospheric Rivers Depicted from Satellite and NWP Reanalysis

Wednesday, 16 December 2015
Poster Hall (Moscone South)
Wenze Yang, University of Maryland, College Park, MD, United States, Ralph R Ferraro, NOAA Center for Satellite Applications and Reserch, SCSB, College Park, United States, Phillip A Arkin, University of Maryland College Park, College Park, MD, United States, Gary A Wick, NOAA Boulder, Boulder, CO, United States and Scientific AR Team of CICS-MD
Atmospheric River (AR) is a recent hot topic in meteorological/atmospheric/hydrological research mostly due to its central role in the global water cycle as they “are responsible for > 90% of all atmospheric water vapor transport in mid-latitudes”, and being identified as the major contributor for the extreme precipitation hitting west coastal areas of the world, including North America, Europe, and North Africa. Further, ARs can be responsible for heavy rainfall events just about anywhere, as the moisture they transport helps to sustain the precipitation forcing from slow moving weather systems. Despite the characterization of ARs to be crucial as a proxy of extreme events, most of the identification done to support weather forecasting applications is done manually through the use of satellite total precipitable water (TPW) imagery, or automatically but in limited regions.

We have developed an objective methodology for detecting AR’s that can be applied to any global field of TPW. Tests have included application to ERA-Interim and the MiRS TPW fields and have shown its utility in detecting global AR’s contributing to recent flooding events across the U.S. and Europe. The importance of such a tool is that a global climatology of AR’s can be developed on both satellite and NWP reanalysis data sets to investigate changes in the characteristics over time in the AR’s – origin regions, length and duration, land falling regions, etc. Here, we’d like to show the depicting AR results of our methodology from several atmospheric humidity data sources: from satellite observations as SSM/I, or blended TPW, and from NWP reanalysis as ERA, or MERRA.