  Minimum central pressure ($P_{min}$) is an integrated measure of the tropical
cyclone wind field and is known to be a useful indicator of storm damage
potential. A simple model that predicts $P_{min}$ from routinely-estimated
quantities, including storm size, would be of great value. Here we present a
simple linear empirical model for predicting $P_{min}$ from maximum wind speed,
the radius of 34-knot winds ($R_{34kt}$), storm-center latitude, and the
environmental pressure. An empirical model for the pressure deficit is first
developed that takes as predictors specific combinations of these quantities
that are derived directly from theory, based on gradient wind balance and a
modified-Rankine-type wind profile known to capture storm structure inside of
$R_{34kt}$. Model coefficients are estimated using data from the southwestern
North Atlantic and eastern North Pacific from 2004--2022 using aircraft-based
estimates of $P_{min}$, Extended Best Track data, and estimates of
environmental pressure from Global Forecast System (GFS) analyses. The model
has near-zero conditional bias even for low $P_{min}$, explaining 94.4\% of the
variance. Performance is superior to a variety of other model formulations,
including a standard wind-pressure model that does not account for storm size
or latitude (89.4\% variance explained). Model performance is also strong when
applied to high-latitude data and data near coastlines. Finally, the model is
shown to perform comparably well in an operations-like setting based solely on
routinely-estimated variables, including the pressure of the outermost closed
isobar. Case study applications to five impactful historical storms are
discussed. Overall, the model offers a simple and fast prediction for $P_{min}$
for practical use in operations and research.
