Despite being the source region of severe weather hazards, the quantification
of the fast current of upward moving air (i.e., updraft) remains unavailable
for operational forecasting. 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 (3D) 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 (SHASH) distribution is adapted to run
with UNets, 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.

通过使用机器学习模型 U-Nets，本研究从三维雷达反射率中提取最大垂直速度及其面积范围，以评估风暴的严重潜力。