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 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.