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\begin{document}

\title{Evaluating the Representations of Atmospheric Rivers and Their
Associated Precipitation in Reanalyses with Satellite Observations}



\author[1,2]{Weiming Ma}%
\author[1]{Gang Chen}%
\author[3,4]{Bin Guan}%
\author[5]{Christine A Shields}%
\author[4]{Baijun Tian}%
\author[1,1]{Emilio Yanez}%
\affil[1]{Department of Atmospheric and Oceanic Sciences, University of California}%
\affil[2]{}%
\affil[3]{Joint Institute for Regional Earth System Science and Engineering, University of California}%
\affil[4]{Jet Propulsion Laboratory, California Institute of Technology}%
\affil[5]{Climate and Global Dynamics Laboratory, National Center for Atmospheric Research}%


\vspace{-1em}



  
  \date{May 26, 2024}


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\begin{abstract}
Atmospheric rivers (ARs) are filaments of enhanced horizontal moisture
transport in the atmosphere. Due to their prominent role in the
meridional moisture transport and regional weather extremes, ARs have
been studied extensively in recent years. Yet, the representations of
ARs and their associated precipitation on a global scale remains largely
unknown. In this study, we developed an AR detection algorithm
specifically for satellite observations using moisture and the
geostrophic winds derived from 3D geopotential height field from the
combined retrievals of the Atmospheric Infrared Sounder and the Advanced
Microwave Sounding Unit on NASA Aqua satellite. This algorithm enables
us to develop the first global AR catalog based solely on satellite
observations. The satellite-based AR catalog is then combined with the
satellite-based precipitation (Integrated Muti-SatellitE Retrievals for
GPM) to evaluate the representations of ARs and AR-induced precipitation
in reanalysis products. Our results show that the spreads in AR
frequency and AR length distribution are generally small across
datasets, while the spread in AR width is relatively larger. In terms of
the AR-induced precipitation, both AR-induced mean and extreme
precipitation are too weak nearly everywhere in reanalyses. However, all
reanalyses tend to precipitate too often under AR conditions, especially
over low latitude regions. This finding is consistent with the
``drizzling'' bias which has plagued generations of climate models.
Overall, the findings of this study can help to improve the
representations of ARs and associated precipitation in reanalyses and
climate models.{}%
\end{abstract}%



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