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\begin{document}

\title{Evaluation of gridded precipitation datasets over international basins
and large lakes}



\author[1]{Yi Hong}%
\author[2]{Eric J Anderson}%
\author[3]{Lauren M Fry}%
\author[4]{Hong Xuan Do}%
\author[5]{James A Kessler}%
\author[6]{Laura K Read}%
\author[7]{Arezoo Rafieei-Nasab}%
\author[8]{Andrew D. Gronewold}%
\author[9]{Lacey Mason}%
\affil[1]{Cooperative Institute for Great Lakes Research}%
\affil[2]{National Oceanic and Atmospheric Administration (NOAA)}%
\affil[3]{University of Michigan–Ann Arbor}%
\affil[4]{University of Michigan}%
\affil[5]{NOAA Great Lakes Environmental Research Laboratory}%
\affil[6]{Tufts University}%
\affil[7]{NCAR}%
\affil[8]{University of Michigan-Ann Arbor}%
\affil[9]{NOAA}%


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  \date{May 25, 2024}


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\begin{abstract}
Reliable precipitation estimates are a crucial backbone for supporting
hydrologic modeling and other geophysical applications. However,
watersheds that extend across international boundaries or those that
contain large bodies of water pose particular challenges to acquiring
consistent and accurate precipitation estimates. The North American
Great Lakes basin is characterized by both of these features, which has
led to long-standing challenges to water budget analysis and hydrologic
prediction. In order to provide optimal conditions for hydrologic model
calibration, retrospective analyses, and real-time forecasting, this
study comprehensively evaluates four gridded datasets over the Great
Lakes basin, including the Analysis of Record for Calibration (AORC),
Canadian Precipitation Analysis (CaPA), Multi-sensor Precipitation
Estimate (MPE), and a merged CaPA-MPE data set, in which these products
are analyzed at multiple spatial and temporal scales using station
observations and a statistical water balance model. In comparison with
gauge observations from the Global Historical Climatology Network Daily
(GHCN-D), gridded datasets generally agree with ground observations,
however the international border clearly delineates a decrease in
gridded precipitation accuracy over the Canadian portion of the basin.
Analysis reveals that rank in gridded precipitation accuracy differs for
overland and overlake regions, and between colder and warmer months.
Overall, the AORC has the lowest variance compared to gauge observations
and has greater performance over temporal and spatial scales. While CaPA
and AORC may better capture atmospheric dynamics between land and lake
regions, comparison with a statistical water balance model suggests that
AORC and MPE provide the best estimates of monthly overlake
precipitation.%
\end{abstract}%



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