  The quantification of storm updrafts remains unavailable for operational
forecasting despite their inherent importance to convection and its associated
severe weather hazards. Updraft proxies, like overshooting top area from
satellite images, have been linked to severe weather hazards but only relate to
a limited portion of the total storm updraft. This study investigates if a
machine learning model, namely U-Nets, can skillfully retrieve maximum vertical
velocity and its areal extent from 3-dimensional gridded radar reflectivity
alone. The machine learning model is trained using simulated radar reflectivity
and vertical velocity from the National Severe Storm Laboratory's convection
permitting Warn on Forecast System (WoFS). A parametric regression technique
using the sinh-arcsinh-normal distribution is adapted to run with U-Nets,
allowing for both deterministic and probabilistic predictions of maximum
vertical velocity. The best models after hyperparameter search provided less
than 50% root mean squared error, a coefficient of determination greater than
0.65 and an intersection over union (IoU) of more than 0.45 on the independent
test set composed of WoFS data. Beyond the WoFS analysis, a case study was
conducted using real radar data and corresponding dual-Doppler analyses of
vertical velocity within a supercell. The U-Net consistently underestimates the
dual-Doppler updraft speed estimates by 50$\%$. Meanwhile, the area of the 5
and 10 m s^-1 updraft cores show an IoU of 0.25. While the above statistics are
not exceptional, the machine learning model enables quick distillation of 3D
radar data that is related to the maximum vertical velocity which could be
useful in assessing a storm's severe potential.
