Weather conditions are often a major determinant of the severity of power outage events. The ability to accurately forecast weather-related outages would allow power companies to reduce outage times for their customers and thereby improve electrical grid reliability. In recent years, the Eversource Energy Center at the University of Connecticut has developed an outage prediction model (OPM) for Eversource service territories in Connecticut. The purpose of this project is to improve outage modelling capabilities for winter events and expand the existing OPM to New Hampshire. The first of three research objectives was to establish a statistical relationship between Eversource’s two outage reporting methods. A comparison between the current Outage Management System (OMS) and the older Trouble Report and Unsatisfactory Performance of Equipment Reports (TRUPERs) was conducted during the overlap period of the two datasets (2016-2017). With the exception of a few outlier events, results of the comparison show a high correlation (R2 = 0.9983) between the two datasets. Identifying major power outages from previous years will expand the OPM event catalog and improve model performance. The second objective was to complete a Weather Research and Forecasting (WRF) Model analysis for all New Hampshire METAR locations by comparing WRF xvi forecasted values of 18 model variables with observed values. WRF simulations were run for a sample of 12 rain/wind and 13 winter outage events that occurred from 2016-2017. The results of this analysis allowed the performance of the Plymouth State WRF model to be assessed and ultimately determined that this configuration of the WRF is compatible with the current OPM. Lastly, a case study analysis was performed to identify winter weather events where both wet snow and wind created widespread power outages. It is hypothesized that wet snow will have a different impact on outages compared to winter weather conditions involving drier, lighter snow or high winds. The two events chosen as case studies were 9–11 December 2009 and 31 March–2 April 2017. Similar winter events will be considered in the future for inclusion in the OPM catalog, which will allow for improved model performance across a spectrum of winter conditions.