Evaluating the Representations of Atmospheric Rivers and Their Associated Precipitation in Reanalyses With Satellite Observations
Atmospheric rivers (ARs) are filaments of enhanced horizontal moisture transport in the atmosphere. Due to their
prominent role in 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 remain largely unknown.
In this study, we developed an AR detection algorithm specifically for satellite observations using moisture and the
geostrophic winds derived from a 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.
The findings of this study can help to improve the representations of ARs and associated precipitation in reanalyses and
climate models.
Our results show that the spreads in AR frequency and AR length distributions are generally small across data sets, while
the spread in AR width is relatively larger. Reanalysis products are found to consistently underestimate both mean and
extreme AR-related precipitation. 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.