A Mesocyclone Detection Algorithm (MDA) and a near-storm environment (NSE) algorithm have been developed at the National Severe Storms Laboratory. The MDA analyzes azimuthal shear in Doppler velocity data in 3 dimensions to identify storm-scale circulations. Sometimes, though not always, these circulations are precursors to tornadoes.~\cite{nn_tda} developed a neural network based on the MDA parameters to identify which of the circulations would be tornadic using a small set of data cases. That work was extended to cover 43 storm days in~\cite{nn_tda2} using a more robust methodology.

In this paper, we further extend the work to use 83 storm days and introduce some variations that improve neural network performance over that achieved by~\cite{twg}. We also evaluate whether the incorporation of near-storm evironment (NSE) data from those days can improve the predictive capability of the neural network.

On an independent test set of 27 storm days, we achieve a Heidke Skill Score (HSS) of 0.41 using just the MDA parameters and a HSS of 0.45 using a combination of MDA and NSE parameters. The Critical Success Index (CSI) for the MDA-only neural network is 0.29, while the CSI for the neural network with both MDA and NSE parameters is 0.32.