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\begin{document}

\title{Do Nudging Tendencies Depend on the Nudging Timescale Chosen in
Atmospheric Models?}



\author[1]{Christopher G Kruse}%
\author[2]{Julio T. Bacmeister}%
\author[3]{Colin M. Zarzycki}%
\author[4]{Vincent E Larson}%
\author[2]{Katherine Thayer-Calder}%
\affil[1]{NorthWest Research Associates}%
\affil[2]{National Center for Atmospheric Research (UCAR)}%
\affil[3]{Pennsylvania State University}%
\affil[4]{University of Wisconsin-Milwaukee}%


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  \date{May 23, 2024}


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\begin{abstract}
Nudging is a ubiquitous capability of numerical weather and climate
models\\
that is widely used in a variety of applications (e.g. crude data
assimilation,\\
``intelligent' interpolation between analysis times, constraining flow\\
in tracer advection/diffusion simulations). Here, the focus is on the\\
momentum nudging tendencies themselves, rather than the atmospheric
state\\
that results from application of the method.\\
The initial intent was to interpret these tendencies as a quantitative\\
estimation of model error (net parameterization error in particular).
However,\\
it was found that nudging tendencies depend strongly on the nudging time
scale\\
chosen, which is the primary result presented here. Reducing the nudging
time\\
scale reduces the difference between the model state and the target
state,\\
but much less so than the reduction in the nudging time scale,
resulting\\
in increased nudging tendencies. The dynamical\\
core, in particular, appears to increasingly oppose nudging tendencies\\
as the nudging time scale is reduced. These results suggest nudging\\
tendencies cannot be \emph{quantitatively} interpreted as model error.
Still,\\
nudging tendencies do contain some information on model errors and/or
missing\\
physical processes and still might be useful in model development and
tuning,\\
even if only qualitatively.%
\end{abstract}%



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